# load "Ask a A Manager 2021 Survey" googlesheet
# https://www.askamanager.org/
ask_a_manager_2021 <- read_csv(here::here("data","ask_a_manager.csv"))
skimr::skim(ask_a_manager_2021)
| Name | ask_a_manager_2021 |
| Number of rows | 26765 |
| Number of columns | 18 |
| _______________________ | |
| Column type frequency: | |
| character | 14 |
| logical | 2 |
| numeric | 1 |
| POSIXct | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| how_old_are_you | 0 | 1.00 | 5 | 10 | 0 | 7 | 0 |
| industry | 62 | 1.00 | 2 | 171 | 0 | 1084 | 0 |
| job_title | 0 | 1.00 | 1 | 126 | 0 | 12838 | 0 |
| additional_context_on_job_title | 19868 | 0.26 | 1 | 781 | 0 | 6612 | 0 |
| currency | 0 | 1.00 | 3 | 7 | 0 | 11 | 0 |
| additional_context_on_income | 23851 | 0.11 | 1 | 1143 | 0 | 2839 | 0 |
| country | 0 | 1.00 | 1 | 209 | 0 | 297 | 0 |
| state | 4761 | 0.82 | 4 | 114 | 0 | 125 | 0 |
| city | 23 | 1.00 | 1 | 171 | 0 | 4070 | 0 |
| overall_years_of_professional_experience | 0 | 1.00 | 9 | 16 | 0 | 8 | 0 |
| years_of_experience_in_field | 0 | 1.00 | 9 | 16 | 0 | 8 | 0 |
| highest_level_of_education_completed | 202 | 0.99 | 3 | 34 | 0 | 6 | 0 |
| gender | 155 | 0.99 | 3 | 29 | 0 | 5 | 0 |
| race | 151 | 0.99 | 5 | 125 | 0 | 47 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| other_monetary_comp | 26765 | 0 | NaN | : |
| currency_other | 26765 | 0 | NaN | : |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| annual_salary | 0 | 1 | 144988 | 5488158 | 0 | 54000 | 75712 | 110000 | 8.7e+08 | ▇▁▁▁▁ |
Variable type: POSIXct
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| timestamp | 0 | 1 | 2021-04-27 11:02:09 | 2021-09-14 21:55:44 | 2021-04-28 12:35:21 | 23989 |
After skimming the dataset, we took the below steps to clean the raw data :
# remove the NA values and 0 salaries
ask_a_manager_2021 <- ask_a_manager_2021 %>%
filter(!gender %in% c("NA","Other or prefer not to answer","Prefer not to answer", "Non-binary", NA) &
race!="NA" &
highest_level_of_education_completed!="NA"&
annual_salary>0)
# using z-score. remove data out of 3 sigma
# ask_a_manager_2021 <- ask_a_manager_2021[-which(abs(scale(ask_a_manager_2021$annual_salary))>3),]
# using quantile. remove data larger than 75%+1.5*IQR or smaller than 25%-1.5*IQR
# we choose this method recommended by the stats community
Q <- quantile(ask_a_manager_2021$annual_salary, probs=c(.25, .75))
iqr <- IQR(ask_a_manager_2021$annual_salary)
up <- Q[2]+1.5*iqr # Upper Range
low <- Q[1]-1.5*iqr # Upper Range
ask_a_manager_2021 <- ask_a_manager_2021 %>%
filter(annual_salary>=low & annual_salary<=up)
We observe there are only 2 samples under 18 years old correctly entered their data, so we remove them to make analysis between age groups possible.
ask_a_manager_2021 %>%
group_by(how_old_are_you, gender) %>%
summarise(count=n()) %>%
ggplot(aes(x=fct_relevel(how_old_are_you,levels=c("under 18","18-24","25-34","35-44","45-54","55-64","65 or over")),y=count))+
geom_col()+
labs(title="Sample size in age groups",
x="Age Group",
y="number of samples")+
NULL
ask_a_manager_2021 <- ask_a_manager_2021 %>%
filter(how_old_are_you!="under 18")
factor datatypeNamely, we want to make age, years of working experience, education into factor, and take the log of salary for the next step of EDA.
ask_a_manager_2021 <- ask_a_manager_2021 %>%
mutate(
country = countrycode::countryname(country),
age_group = factor(how_old_are_you,levels=c("18-24","25-34","35-44","45-54","55-64","65 or over")),
field_exp = factor(years_of_experience_in_field,levels=c("1 year or less","2 - 4 years","5-7 years","8 - 10 years","11 - 20 years","21 - 30 years","31 - 40 years","41 years or more")),
pro_exp = factor(overall_years_of_professional_experience,levels=c("1 year or less","2 - 4 years","5-7 years","8 - 10 years","11 - 20 years","21 - 30 years","31 - 40 years","41 years or more")),
education = factor(highest_level_of_education_completed, levels=c("High School","Some college","College degree","Master's degree","Professional degree (MD, JD, etc.)", "PhD")),
higher_edu = ifelse(education %in% c("College degree","Master's degree", "PhD", "Professional degree (MD, JD, etc.)"),1,0),
salary = annual_salary,
# take ln() to annual salary
log_salary = log(annual_salary)
) %>%
janitor::clean_names() # clean columns names
# there are people under 18 having more than 18 years of experience
ask_a_manager_2021 %>%
filter(age_group=="under 18") %>%
select(c("age_group","overall_years_of_professional_experience"))
| age_group | overall_years_of_professional_experience |
|---|
ask_a_manager_2021 <- ask_a_manager_2021 %>%
mutate(max_age = case_when(how_old_are_you=="under 18"~18,
how_old_are_you=="65 or over"~999,
T~as.numeric(substr(how_old_are_you,4,5))),
# parse the first number in a character
min_exp = if_else(parse_number(overall_years_of_professional_experience)>
parse_number(years_of_experience_in_field),
parse_number(overall_years_of_professional_experience),
parse_number(years_of_experience_in_field))) %>%
# delete the problematic entries and the tow columns created here.
filter(max_age>=min_exp) %>%
select(-c("max_age","min_exp"))
Since there are over 1000 reported industries in the dataset, we will only keep the top 25 industries which account for 92% of total entries
# Industry is messy... it has > 1000 different industries.
# we only keep the names of top 25 industries. (24597(92%) out of 26765 entries)
industry_list <- ask_a_manager_2021 %>%
count(industry, sort=TRUE) %>%
mutate(percent = 100* n/sum(n)) %>%
slice_max(order_by = n, n = 25) %>%
select(industry)
ask_a_manager_2021 <- ask_a_manager_2021 %>%
mutate(industry = ifelse(industry %in% industry_list$industry,
industry,
"Others"))
Since there are 11 different currencies reported in the dataset, we want to unify the most common currencies into USD for the convenience of comparison analysis afterwards. We decided to convert all currencies with occurences greater than 100 into USD. This included CAD,GBP,EUR,AUD/NZD. Other was not converted as there wasn’t specific information on the currency.
ask_a_manager_2021 %>%
count(currency, sort=TRUE) %>%
mutate(percent = 100* n/sum(n))
| currency | n | percent |
|---|---|---|
| USD | 20268 | 83.6 |
| CAD | 1474 | 6.08 |
| GBP | 1434 | 5.91 |
| EUR | 551 | 2.27 |
| AUD/NZD | 432 | 1.78 |
| Other | 57 | 0.235 |
| CHF | 29 | 0.12 |
| SEK | 1 | 0.00412 |
From the above table, We can see that USD, CAD, GBP, EUR, (AUD/NZD) are the most common currencies. We will convert the currencies all to USD using exchange rate data from library quantmod, and the exchange rate will be taken from the last date of the survey “2021-09-14”
#gets the currency rate for the currency pairs. This uses the Oanda which keeps currency data for the last 180 days.
#In 180 days from "2021-09-14" the code will no longer return the currency rates.
currencies <-getSymbols(c("EUR/USD","CAD/USD","GBP/USD","AUD/USD" , "NZD/USD"),
from="2021-09-14", to="2021-09-14", src="oanda")
#In order to preserve the analysis for after 180 days we can hard code the currency rates into a tibble
currencies_preserved <- tibble(
name = c("EUR/USD","CAD/USD","GBP/USD","AUD/USD" , "NZD/USD"),
value = c(1.18,0.79,1.38,0.734,0.711)
)
Here we create a new column called converted_salary which converts the 5 most common currencies in USD. These are then rounded to the nearest whole number.
#the values are taken from the currencies_preserved$value as to preserve the analysis when
# getSymbols will no longer work. The index matches the currency pair.
ask_a_manager_2021 <- ask_a_manager_2021 %>%
mutate(converted_salary = case_when(
currency == "GBP" ~ round(annual_salary*currencies_preserved$value[3]),
currency == "AUD/NZD" & country == "Australia" ~ round(annual_salary*currencies_preserved$value[4]),
currency == "AUD/NZD" & country == "New Zealand" ~ round(annual_salary*currencies_preserved$value[5]),
currency == "CAD" ~ round(annual_salary*currencies_preserved$value[2]),
currency == "EUR" ~ round(annual_salary*currencies_preserved$value[1]),
TRUE ~ round(annual_salary)
))
#Here we filter the dataset to only include the salaries that were converted into USD terms. The other currencies also included lots of outliers which we also get rid of here.
ask_a_manager_2021 <- ask_a_manager_2021 %>%
filter(currency %in% c("USD","GBP","AUD/NZD","EUR","CAD") &
!is.na(country))
ask_a_manager_2021 %>%
count(currency, sort=TRUE) %>%
mutate(percent = 100* n/sum(n))
| currency | n | percent |
|---|---|---|
| USD | 20128 | 84.1 |
| CAD | 1464 | 6.12 |
| GBP | 1362 | 5.69 |
| EUR | 547 | 2.29 |
| AUD/NZD | 428 | 1.79 |
# Some quick counts, groups, etc
ask_a_manager_2021 %>%
count(age_group, sort=TRUE) %>%
mutate(percent = 100* n/sum(n)) %>%
ggplot(aes(x=fct_relevel(age_group,levels=c("under 18","18-24","25-34","35-44","45-54","55-64","65 or over")), y=n)) +
<<<<<<< HEAD
geom_bar(stat="identity") +
labs(title = "Age distribution of Ask a Manager survey respondents",
x="Age group",
y="Frequency",
caption = "Source:Askamanager.com")
# 'country'
ask_a_manager_2021 %>%
count(country, sort=TRUE) %>%
mutate(percent = 100* n/sum(n))
| country | n | percent |
|---|---|---|
| US | 19988 | 83.5 |
| Canada | 1477 | 6.17 |
| UK | 1368 | 5.72 |
| Australia | 338 | 1.41 |
| Germany | 166 | 0.694 |
| Ireland | 111 | 0.464 |
| New Zealand | 100 | 0.418 |
| Netherlands | 76 | 0.318 |
| France | 56 | 0.234 |
| Spain | 41 | 0.171 |
| Belgium | 32 | 0.134 |
| Austria | 14 | 0.0585 |
| Finland | 14 | 0.0585 |
| Italy | 13 | 0.0543 |
| Denmark | 7 | 0.0293 |
| Israel | 6 | 0.0251 |
| Greece | 5 | 0.0209 |
| Japan | 5 | 0.0209 |
| Portugal | 5 | 0.0209 |
| Sweden | 5 | 0.0209 |
| Brazil | 4 | 0.0167 |
| India | 4 | 0.0167 |
| Mexico | 4 | 0.0167 |
| Puerto Rico | 4 | 0.0167 |
| Romania | 4 | 0.0167 |
| Singapore | 4 | 0.0167 |
| Latvia | 3 | 0.0125 |
| Luxembourg | 3 | 0.0125 |
| Pakistan | 3 | 0.0125 |
| Philippines | 3 | 0.0125 |
| South Africa | 3 | 0.0125 |
| Switzerland | 3 | 0.0125 |
| Bermuda | 2 | 0.00836 |
| Bulgaria | 2 | 0.00836 |
| China | 2 | 0.00836 |
| Estonia | 2 | 0.00836 |
| Ghana | 2 | 0.00836 |
| Kenya | 2 | 0.00836 |
| Lithuania | 2 | 0.00836 |
| Malaysia | 2 | 0.00836 |
| Norway | 2 | 0.00836 |
| Poland | 2 | 0.00836 |
| Thailand | 2 | 0.00836 |
| Afghanistan | 1 | 0.00418 |
| Argentina | 1 | 0.00418 |
| Bahamas | 1 | 0.00418 |
| Bangladesh | 1 | 0.00418 |
| Cambodia | 1 | 0.00418 |
| Cayman Islands | 1 | 0.00418 |
| Chile | 1 | 0.00418 |
| Costa Rica | 1 | 0.00418 |
| Côte d’Ivoire | 1 | 0.00418 |
| Croatia | 1 | 0.00418 |
| Cuba | 1 | 0.00418 |
| Ecuador | 1 | 0.00418 |
| Eritrea | 1 | 0.00418 |
| Hong Kong | 1 | 0.00418 |
| Hungary | 1 | 0.00418 |
| Isle of Man | 1 | 0.00418 |
| Jamaica | 1 | 0.00418 |
| Jordan | 1 | 0.00418 |
| Kuwait | 1 | 0.00418 |
| Malta | 1 | 0.00418 |
| Morocco | 1 | 0.00418 |
| Nigeria | 1 | 0.00418 |
| Panama | 1 | 0.00418 |
| Qatar | 1 | 0.00418 |
| Russia | 1 | 0.00418 |
| Rwanda | 1 | 0.00418 |
| Serbia | 1 | 0.00418 |
| Sierra Leone | 1 | 0.00418 |
| Slovakia | 1 | 0.00418 |
| Slovenia | 1 | 0.00418 |
| Somalia | 1 | 0.00418 |
| South Korea | 1 | 0.00418 |
| Sri Lanka | 1 | 0.00418 |
| Uganda | 1 | 0.00418 |
| Ukraine | 1 | 0.00418 |
| United Arab Emirates | 1 | 0.00418 |
| Uruguay | 1 | 0.00418 |
| Vietnam | 1 | 0.00418 |
| city | n | percent |
|---|---|---|
| Boston | 726 | 3.03 |
| Chicago | 710 | 2.97 |
| New York | 650 | 2.72 |
| Seattle | 608 | 2.54 |
| London | 548 | 2.29 |
| New York City | 432 | 1.81 |
| Los Angeles | 429 | 1.79 |
| San Francisco | 409 | 1.71 |
| Portland | 398 | 1.66 |
| Toronto | 374 | 1.56 |
| Minneapolis | 333 | 1.39 |
| Washington | 330 | 1.38 |
| Austin | 304 | 1.27 |
| Philadelphia | 303 | 1.27 |
| Atlanta | 294 | 1.23 |
| Denver | 279 | 1.17 |
| Washington, DC | 233 | 0.974 |
| Houston | 226 | 0.944 |
| Vancouver | 205 | 0.857 |
| Pittsburgh | 202 | 0.844 |
| Dallas | 199 | 0.832 |
| NYC | 182 | 0.761 |
| Cambridge | 181 | 0.756 |
| Madison | 175 | 0.731 |
| Columbus | 173 | 0.723 |
| San Diego | 163 | 0.681 |
| Baltimore | 148 | 0.618 |
| Indianapolis | 147 | 0.614 |
| Washington DC | 142 | 0.593 |
| Raleigh | 136 | 0.568 |
| Cleveland | 134 | 0.56 |
| Ottawa | 130 | 0.543 |
| Richmond | 128 | 0.535 |
| Phoenix | 121 | 0.506 |
| Remote | 114 | 0.476 |
| Arlington | 109 | 0.456 |
| Durham | 106 | 0.443 |
| Nashville | 103 | 0.43 |
| St. Louis | 102 | 0.426 |
| Cincinnati | 101 | 0.422 |
| DC | 100 | 0.418 |
| Sacramento | 97 | 0.405 |
| Melbourne | 96 | 0.401 |
| Milwaukee | 95 | 0.397 |
| Charlotte | 92 | 0.384 |
| Detroit | 90 | 0.376 |
| Manchester | 88 | 0.368 |
| Oakland | 85 | 0.355 |
| Salt Lake City | 85 | 0.355 |
| Rochester | 83 | 0.347 |
| Kansas City | 79 | 0.33 |
| Orlando | 79 | 0.33 |
| San Jose | 79 | 0.33 |
| Louisville | 78 | 0.326 |
| Sydney | 78 | 0.326 |
| Alexandria | 74 | 0.309 |
| Dublin | 74 | 0.309 |
| Ann Arbor | 72 | 0.301 |
| Montreal | 69 | 0.288 |
| Calgary | 68 | 0.284 |
| Tucson | 68 | 0.284 |
| Boulder | 66 | 0.276 |
| Des Moines | 65 | 0.272 |
| Tampa | 65 | 0.272 |
| Columbia | 64 | 0.267 |
| Birmingham | 63 | 0.263 |
| Omaha | 62 | 0.259 |
| Edmonton | 61 | 0.255 |
| St. Paul | 60 | 0.251 |
| Albuquerque | 55 | 0.23 |
| San Antonio | 55 | 0.23 |
| New Orleans | 54 | 0.226 |
| Albany | 53 | 0.221 |
| Springfield | 53 | 0.221 |
| Brooklyn | 52 | 0.217 |
| Grand Rapids | 52 | 0.217 |
| Lexington | 52 | 0.217 |
| Buffalo | 49 | 0.205 |
| Irvine | 48 | 0.201 |
| Jacksonville | 48 | 0.201 |
| Las Vegas | 46 | 0.192 |
| Memphis | 46 | 0.192 |
| Rockville | 46 | 0.192 |
| Hartford | 45 | 0.188 |
| Winnipeg | 45 | 0.188 |
| Berlin | 44 | 0.184 |
| Edinburgh | 44 | 0.184 |
| Bristol | 43 | 0.18 |
| Brisbane | 42 | 0.176 |
| Oklahoma City | 42 | 0.176 |
| Saint Paul | 42 | 0.176 |
| Boise | 41 | 0.171 |
| Burlington | 41 | 0.171 |
| Halifax | 41 | 0.171 |
| Wilmington | 41 | 0.171 |
| Bloomington | 40 | 0.167 |
| Fort Worth | 40 | 0.167 |
| Wellington | 40 | 0.167 |
| Bellevue | 39 | 0.163 |
| N/A | 39 | 0.163 |
| Leeds | 38 | 0.159 |
| Bethesda | 37 | 0.155 |
| Oxford | 37 | 0.155 |
| Redmond | 37 | 0.155 |
| Amsterdam | 36 | 0.15 |
| Colorado Springs | 36 | 0.15 |
| Providence | 36 | 0.15 |
| Syracuse | 36 | 0.15 |
| Tulsa | 36 | 0.15 |
| Wichita | 36 | 0.15 |
| Harrisburg | 35 | 0.146 |
| Charleston | 34 | 0.142 |
| Fort Collins | 34 | 0.142 |
| Charlottesville | 33 | 0.138 |
| Anchorage | 32 | 0.134 |
| Fairfax | 32 | 0.134 |
| Bay Area | 31 | 0.13 |
| Glasgow | 31 | 0.13 |
| Huntsville | 31 | 0.13 |
| Paris | 31 | 0.13 |
| Auckland | 30 | 0.125 |
| Lansing | 30 | 0.125 |
| Tacoma | 30 | 0.125 |
| Washington, D.C. | 30 | 0.125 |
| Dayton | 29 | 0.121 |
| Newark | 29 | 0.121 |
| Salem | 29 | 0.121 |
| Knoxville | 28 | 0.117 |
| Miami | 28 | 0.117 |
| Saint Louis | 28 | 0.117 |
| Silver Spring | 28 | 0.117 |
| Athens | 27 | 0.113 |
| Eugene | 27 | 0.113 |
| Greensboro | 27 | 0.113 |
| Lincoln | 27 | 0.113 |
| Reston | 27 | 0.113 |
| Victoria | 27 | 0.113 |
| Munich | 26 | 0.109 |
| Perth | 26 | 0.109 |
| Adelaide | 25 | 0.104 |
| Chapel Hill | 25 | 0.104 |
| Berkeley | 24 | 0.1 |
| Missoula | 24 | 0.1 |
| New Haven | 24 | 0.1 |
| Princeton | 24 | 0.1 |
| Gainesville | 23 | 0.0961 |
| Greenville | 23 | 0.0961 |
| Spokane | 23 | 0.0961 |
| Olympia | 22 | 0.0919 |
| Santa Clara | 22 | 0.0919 |
| Palo Alto | 21 | 0.0878 |
| Toledo | 21 | 0.0878 |
| Vienna | 21 | 0.0878 |
| Waltham | 21 | 0.0878 |
| Annapolis | 20 | 0.0836 |
| Canberra | 20 | 0.0836 |
| Chattanooga | 20 | 0.0836 |
| District of Columbia | 20 | 0.0836 |
| St Louis | 20 | 0.0836 |
| Troy | 20 | 0.0836 |
| Worcester | 20 | 0.0836 |
| 20 | 0.0836 | |
| Brussels | 19 | 0.0794 |
| Lafayette | 19 | 0.0794 |
| Little Rock | 19 | 0.0794 |
| Nottingham | 19 | 0.0794 |
| Scottsdale | 19 | 0.0794 |
| South Bend | 19 | 0.0794 |
| Baton Rouge | 18 | 0.0752 |
| Brighton | 18 | 0.0752 |
| Long Beach | 18 | 0.0752 |
| Manhattan | 18 | 0.0752 |
| McLean | 18 | 0.0752 |
| Mountain View | 18 | 0.0752 |
| Norfolk | 18 | 0.0752 |
| Overland Park | 18 | 0.0752 |
| Plano | 18 | 0.0752 |
| Prefer not to answer | 18 | 0.0752 |
| York | 18 | 0.0752 |
| Augusta | 17 | 0.071 |
| College Park | 17 | 0.071 |
| Frederick | 17 | 0.071 |
| Iowa City | 17 | 0.071 |
| Lancaster | 17 | 0.071 |
| Sunnyvale | 17 | 0.071 |
| Barcelona | 16 | 0.0669 |
| Champaign | 16 | 0.0669 |
| Concord | 16 | 0.0669 |
| Everett | 16 | 0.0669 |
| Fresno | 16 | 0.0669 |
| Honolulu | 16 | 0.0669 |
| Savannah | 16 | 0.0669 |
| Sheffield | 16 | 0.0669 |
| Tallahassee | 16 | 0.0669 |
| Tempe | 16 | 0.0669 |
| Waterloo | 16 | 0.0669 |
| Akron | 15 | 0.0627 |
| Reno | 15 | 0.0627 |
| Santa Cruz | 15 | 0.0627 |
| Saskatoon | 15 | 0.0627 |
| Allentown | 14 | 0.0585 |
| Beaverton | 14 | 0.0585 |
| Fort Lauderdale | 14 | 0.0585 |
| Hamilton | 14 | 0.0585 |
| Hillsboro | 14 | 0.0585 |
| Jackson | 14 | 0.0585 |
| Kitchener | 14 | 0.0585 |
| Liverpool | 14 | 0.0585 |
| Lowell | 14 | 0.0585 |
| Newton | 14 | 0.0585 |
| Reading | 14 | 0.0585 |
| San Francisco Bay Area | 14 | 0.0585 |
| San Mateo | 14 | 0.0585 |
| St Paul | 14 | 0.0585 |
| Alpharetta | 13 | 0.0543 |
| Asheville | 13 | 0.0543 |
| Bellingham | 13 | 0.0543 |
| Bethlehem | 13 | 0.0543 |
| Canton | 13 | 0.0543 |
| Fayetteville | 13 | 0.0543 |
| Fort Wayne | 13 | 0.0543 |
| Gaithersburg | 13 | 0.0543 |
| Hamburg | 13 | 0.0543 |
| Kalamazoo | 13 | 0.0543 |
| King of Prussia | 13 | 0.0543 |
| n/a | 13 | 0.0543 |
| new york | 13 | 0.0543 |
| Orange | 13 | 0.0543 |
| Pensacola | 13 | 0.0543 |
| Plymouth | 13 | 0.0543 |
| Regina | 13 | 0.0543 |
| Southampton | 13 | 0.0543 |
| Vancouver, BC | 13 | 0.0543 |
| Washington D.C. | 13 | 0.0543 |
| Aberdeen | 12 | 0.0501 |
| Aurora | 12 | 0.0501 |
| Cambridge, MA | 12 | 0.0501 |
| Cary | 12 | 0.0501 |
| Christchurch | 12 | 0.0501 |
| East Lansing | 12 | 0.0501 |
| Ithaca | 12 | 0.0501 |
| Lakewood | 12 | 0.0501 |
| Lehi | 12 | 0.0501 |
| Medford | 12 | 0.0501 |
| Morristown | 12 | 0.0501 |
| New york | 12 | 0.0501 |
| Newcastle | 12 | 0.0501 |
| Northampton | 12 | 0.0501 |
| Pasadena | 12 | 0.0501 |
| portland | 12 | 0.0501 |
| Portsmouth | 12 | 0.0501 |
| Prefer not to say | 12 | 0.0501 |
| SF Bay Area | 12 | 0.0501 |
| Stamford | 12 | 0.0501 |
| Belfast | 11 | 0.046 |
| Chicago suburbs | 11 | 0.046 |
| Cork | 11 | 0.046 |
| Corvallis | 11 | 0.046 |
| Evanston | 11 | 0.046 |
| Falls Church | 11 | 0.046 |
| Fargo | 11 | 0.046 |
| Frisco | 11 | 0.046 |
| Green Bay | 11 | 0.046 |
| Hanover | 11 | 0.046 |
| Herndon | 11 | 0.046 |
| Idaho Falls | 11 | 0.046 |
| Jersey City | 11 | 0.046 |
| Nashua | 11 | 0.046 |
| Ontario | 11 | 0.046 |
| Richland | 11 | 0.046 |
| Santa Fe | 11 | 0.046 |
| Santa Rosa | 11 | 0.046 |
| Sarasota | 11 | 0.046 |
| Twin Cities | 11 | 0.046 |
| Windsor | 11 | 0.046 |
| Andover | 10 | 0.0418 |
| Appleton | 10 | 0.0418 |
| Auburn | 10 | 0.0418 |
| Bentonville | 10 | 0.0418 |
| Boston, MA | 10 | 0.0418 |
| Broomfield | 10 | 0.0418 |
| College Station | 10 | 0.0418 |
| Denton | 10 | 0.0418 |
| DFW | 10 | 0.0418 |
| Fairbanks | 10 | 0.0418 |
| Irving | 10 | 0.0418 |
| La Crosse | 10 | 0.0418 |
| Middletown | 10 | 0.0418 |
| Needham, MA | 10 | 0.0418 |
| Norwich | 10 | 0.0418 |
| Poughkeepsie | 10 | 0.0418 |
| prefer not to answer | 10 | 0.0418 |
| Renton | 10 | 0.0418 |
| Rockford | 10 | 0.0418 |
| Scranton | 10 | 0.0418 |
| Somerset | 10 | 0.0418 |
| South San Francisco | 10 | 0.0418 |
| Southfield | 10 | 0.0418 |
| Topeka | 10 | 0.0418 |
| Utrecht | 10 | 0.0418 |
| Williamsburg | 10 | 0.0418 |
| Winston-Salem | 10 | 0.0418 |
| Ames | 9 | 0.0376 |
| Cedar Rapids | 9 | 0.0376 |
| Chandler | 9 | 0.0376 |
| D.C. | 9 | 0.0376 |
| Kent | 9 | 0.0376 |
| Lawrence | 9 | 0.0376 |
| Madrid | 9 | 0.0376 |
| Manassas | 9 | 0.0376 |
| Morgantown | 9 | 0.0376 |
| Naperville | 9 | 0.0376 |
| Newport | 9 | 0.0376 |
| Orange County | 9 | 0.0376 |
| Orem | 9 | 0.0376 |
| Parsippany | 9 | 0.0376 |
| Portland, OR | 9 | 0.0376 |
| Redwood City | 9 | 0.0376 |
| remote | 9 | 0.0376 |
| Schaumburg | 9 | 0.0376 |
| Somerville | 9 | 0.0376 |
| Trenton | 9 | 0.0376 |
| Waco | 9 | 0.0376 |
| Watertown | 9 | 0.0376 |
| West Palm Beach | 9 | 0.0376 |
| Bangor | 8 | 0.0334 |
| Bath | 8 | 0.0334 |
| Boston area | 8 | 0.0334 |
| Bothell | 8 | 0.0334 |
| Bozeman | 8 | 0.0334 |
| Cardiff | 8 | 0.0334 |
| chicago | 8 | 0.0334 |
| Chicago Suburbs | 8 | 0.0334 |
| Cupertino | 8 | 0.0334 |
| Davis | 8 | 0.0334 |
| Duluth | 8 | 0.0334 |
| Edmonton, Alberta | 8 | 0.0334 |
| Exeter | 8 | 0.0334 |
| Fairfield | 8 | 0.0334 |
| Gloucester | 8 | 0.0334 |
| Golden | 8 | 0.0334 |
| Guelph | 8 | 0.0334 |
| Leicester | 8 | 0.0334 |
| Lewiston | 8 | 0.0334 |
| Long Island | 8 | 0.0334 |
| Metro Detroit | 8 | 0.0334 |
| Milton Keynes | 8 | 0.0334 |
| Mobile | 8 | 0.0334 |
| Montgomery | 8 | 0.0334 |
| Na | 8 | 0.0334 |
| Newcastle upon Tyne | 8 | 0.0334 |
| Peoria | 8 | 0.0334 |
| Philadelphia suburbs | 8 | 0.0334 |
| Provo | 8 | 0.0334 |
| Redding | 8 | 0.0334 |
| Sioux Falls | 8 | 0.0334 |
| Spartanburg | 8 | 0.0334 |
| St. Petersburg | 8 | 0.0334 |
| Surrey | 8 | 0.0334 |
| Walnut Creek | 8 | 0.0334 |
| Woburn | 8 | 0.0334 |
| - | 7 | 0.0293 |
| Amherst | 7 | 0.0293 |
| Boca Raton | 7 | 0.0293 |
| Carson City | 7 | 0.0293 |
| Coventry | 7 | 0.0293 |
| Decatur | 7 | 0.0293 |
| Dubuque | 7 | 0.0293 |
| Düsseldorf | 7 | 0.0293 |
| Englewood | 7 | 0.0293 |
| Florence | 7 | 0.0293 |
| Fort Myers | 7 | 0.0293 |
| Frankfurt | 7 | 0.0293 |
| Franklin | 7 | 0.0293 |
| Glendale | 7 | 0.0293 |
| Helena | 7 | 0.0293 |
| Helsinki | 7 | 0.0293 |
| Kingston | 7 | 0.0293 |
| Laramie | 7 | 0.0293 |
| Lenexa | 7 | 0.0293 |
| Los angeles | 7 | 0.0293 |
| Mclean | 7 | 0.0293 |
| Midland | 7 | 0.0293 |
| Modesto | 7 | 0.0293 |
| Moline | 7 | 0.0293 |
| Morrisville | 7 | 0.0293 |
| N/a | 7 | 0.0293 |
| New York, NY | 7 | 0.0293 |
| Norman | 7 | 0.0293 |
| Ottawa, Ontario | 7 | 0.0293 |
| Richardson | 7 | 0.0293 |
| Riverside | 7 | 0.0293 |
| Roanoke | 7 | 0.0293 |
| Rural | 7 | 0.0293 |
| san francisco | 7 | 0.0293 |
| Suitland | 7 | 0.0293 |
| Toronto, Ontario | 7 | 0.0293 |
| Tuscaloosa | 7 | 0.0293 |
| White Plains | 7 | 0.0293 |
| Amarillo | 6 | 0.0251 |
| Anaheim | 6 | 0.0251 |
| Bedford | 6 | 0.0251 |
| Binghamton | 6 | 0.0251 |
| Blacksburg | 6 | 0.0251 |
| Carrollton | 6 | 0.0251 |
| Chantilly | 6 | 0.0251 |
| Chelmsford | 6 | 0.0251 |
| Chester | 6 | 0.0251 |
| Costa Mesa | 6 | 0.0251 |
| Draper | 6 | 0.0251 |
| El Paso | 6 | 0.0251 |
| Erie | 6 | 0.0251 |
| Framingham | 6 | 0.0251 |
| Greenbelt | 6 | 0.0251 |
| Harrisonburg | 6 | 0.0251 |
| Henderson | 6 | 0.0251 |
| Laurel | 6 | 0.0251 |
| Lebanon | 6 | 0.0251 |
| Leesburg | 6 | 0.0251 |
| Logan | 6 | 0.0251 |
| Lynchburg | 6 | 0.0251 |
| Marietta | 6 | 0.0251 |
| Natick | 6 | 0.0251 |
| Needham | 6 | 0.0251 |
| Novi | 6 | 0.0251 |
| Nyc | 6 | 0.0251 |
| Ogden | 6 | 0.0251 |
| Pocatello | 6 | 0.0251 |
| prefer not to say | 6 | 0.0251 |
| Pullman | 6 | 0.0251 |
| Quincy | 6 | 0.0251 |
| Saginaw | 6 | 0.0251 |
| San Marcos | 6 | 0.0251 |
| Santa Barbara | 6 | 0.0251 |
| seattle | 6 | 0.0251 |
| Seattle, WA | 6 | 0.0251 |
| SF | 6 | 0.0251 |
| Shelton | 6 | 0.0251 |
| Towson | 6 | 0.0251 |
| Tyler | 6 | 0.0251 |
| Various | 6 | 0.0251 |
| Verona | 6 | 0.0251 |
| Virginia Beach | 6 | 0.0251 |
| Warwick | 6 | 0.0251 |
| West Lafayette | 6 | 0.0251 |
| Woodstock | 6 | 0.0251 |
| Alameda | 5 | 0.0209 |
| Ashland | 5 | 0.0209 |
| Atlanta metro area | 5 | 0.0209 |
| Beaumont | 5 | 0.0209 |
| Billings | 5 | 0.0209 |
| boston | 5 | 0.0209 |
| Boston Area | 5 | 0.0209 |
| Broken Arrow | 5 | 0.0209 |
| Burbank | 5 | 0.0209 |
| Carlsbad | 5 | 0.0209 |
| Cherry Hill | 5 | 0.0209 |
| Chesterfield | 5 | 0.0209 |
| Chico | 5 | 0.0209 |
| Claremont | 5 | 0.0209 |
| Corpus Christi | 5 | 0.0209 |
| Daytona Beach | 5 | 0.0209 |
| Eagan | 5 | 0.0209 |
| Escondido | 5 | 0.0209 |
| Fredericksburg | 5 | 0.0209 |
| Fremont | 5 | 0.0209 |
| Grand Forks | 5 | 0.0209 |
| Greater Toronto Area | 5 | 0.0209 |
| Hampton | 5 | 0.0209 |
| Holland | 5 | 0.0209 |
| Home | 5 | 0.0209 |
| Horsham | 5 | 0.0209 |
| indianapolis | 5 | 0.0209 |
| Juneau | 5 | 0.0209 |
| Kennewick | 5 | 0.0209 |
| Kenosha | 5 | 0.0209 |
| Kirkland | 5 | 0.0209 |
| Lakeland | 5 | 0.0209 |
| Leawood | 5 | 0.0209 |
| Lisbon | 5 | 0.0209 |
| London, UK | 5 | 0.0209 |
| Lyon | 5 | 0.0209 |
| Mankato | 5 | 0.0209 |
| Marlborough | 5 | 0.0209 |
| Mason | 5 | 0.0209 |
| Menlo Park | 5 | 0.0209 |
| Milan | 5 | 0.0209 |
| Milford | 5 | 0.0209 |
| Murfreesboro | 5 | 0.0209 |
| New Brunswick | 5 | 0.0209 |
| New York city | 5 | 0.0209 |
| Northfield | 5 | 0.0209 |
| Norwalk | 5 | 0.0209 |
| Pleasanton | 5 | 0.0209 |
| Rock Hill | 5 | 0.0209 |
| Roseville | 5 | 0.0209 |
| San Luis Obispo | 5 | 0.0209 |
| Sanford | 5 | 0.0209 |
| Santa Ana | 5 | 0.0209 |
| Santa Monica | 5 | 0.0209 |
| Shreveport | 5 | 0.0209 |
| Small town | 5 | 0.0209 |
| St. Louis, MO | 5 | 0.0209 |
| Stafford | 5 | 0.0209 |
| State College | 5 | 0.0209 |
| Traverse City | 5 | 0.0209 |
| Utica | 5 | 0.0209 |
| Ventura | 5 | 0.0209 |
| washington | 5 | 0.0209 |
| West Chester | 5 | 0.0209 |
| Ypsilanti | 5 | 0.0209 |
| Aiken | 4 | 0.0167 |
| Aliso Viejo | 4 | 0.0167 |
| Batavia | 4 | 0.0167 |
| Bay City | 4 | 0.0167 |
| Blaine | 4 | 0.0167 |
| Bournemouth | 4 | 0.0167 |
| Bowling Green | 4 | 0.0167 |
| Bradenton | 4 | 0.0167 |
| Bremerton | 4 | 0.0167 |
| Brentwood | 4 | 0.0167 |
| Bronx | 4 | 0.0167 |
| Canada | 4 | 0.0167 |
| Chicago area | 4 | 0.0167 |
| Chicago, IL | 4 | 0.0167 |
| Chicagoland | 4 | 0.0167 |
| Clemson | 4 | 0.0167 |
| Cologne | 4 | 0.0167 |
| Conshohocken | 4 | 0.0167 |
| Copenhagen | 4 | 0.0167 |
| Danbury | 4 | 0.0167 |
| Dc | 4 | 0.0167 |
| Dover | 4 | 0.0167 |
| Downers Grove | 4 | 0.0167 |
| Easton | 4 | 0.0167 |
| Eau Claire | 4 | 0.0167 |
| Eden Prairie | 4 | 0.0167 |
| Edmonds | 4 | 0.0167 |
| Elizabethtown | 4 | 0.0167 |
| Evansville | 4 | 0.0167 |
| Exton | 4 | 0.0167 |
| Farmington Hills | 4 | 0.0167 |
| Flagstaff | 4 | 0.0167 |
| Florham Park | 4 | 0.0167 |
| Folsom | 4 | 0.0167 |
| Frankfort | 4 | 0.0167 |
| Galway | 4 | 0.0167 |
| Georgetown | 4 | 0.0167 |
| Gilbert | 4 | 0.0167 |
| Greeley | 4 | 0.0167 |
| Greenfield | 4 | 0.0167 |
| GTA | 4 | 0.0167 |
| Hamilton, Ontario | 4 | 0.0167 |
| Hoboken | 4 | 0.0167 |
| Issaquah | 4 | 0.0167 |
| Jonesboro | 4 | 0.0167 |
| Joplin | 4 | 0.0167 |
| Katy | 4 | 0.0167 |
| Keene | 4 | 0.0167 |
| Kelowna | 4 | 0.0167 |
| Las Cruces | 4 | 0.0167 |
| Lawrenceville | 4 | 0.0167 |
| Lisle | 4 | 0.0167 |
| Longmont | 4 | 0.0167 |
| Longview | 4 | 0.0167 |
| Los Alamos | 4 | 0.0167 |
| los angeles | 4 | 0.0167 |
| Marion | 4 | 0.0167 |
| Meridian | 4 | 0.0167 |
| Metairie | 4 | 0.0167 |
| Milwaukee area | 4 | 0.0167 |
| Minnetonka | 4 | 0.0167 |
| Mississauga | 4 | 0.0167 |
| Moncton | 4 | 0.0167 |
| Montgomery County | 4 | 0.0167 |
| Montpelier | 4 | 0.0167 |
| Montréal | 4 | 0.0167 |
| Moorhead | 4 | 0.0167 |
| na | 4 | 0.0167 |
| Nanaimo | 4 | 0.0167 |
| Newport News | 4 | 0.0167 |
| Niles | 4 | 0.0167 |
| North Chicago | 4 | 0.0167 |
| Northbrook | 4 | 0.0167 |
| Oak Ridge | 4 | 0.0167 |
| Olathe | 4 | 0.0167 |
| phoenix | 4 | 0.0167 |
| Pomona | 4 | 0.0167 |
| Port Orchard | 4 | 0.0167 |
| Portland, ME | 4 | 0.0167 |
| Puyallup | 4 | 0.0167 |
| Rapid City | 4 | 0.0167 |
| Regional | 4 | 0.0167 |
| Research Triangle Park | 4 | 0.0167 |
| rural | 4 | 0.0167 |
| Rural area | 4 | 0.0167 |
| Rural Ontario | 4 | 0.0167 |
| Salina | 4 | 0.0167 |
| Salt lake city | 4 | 0.0167 |
| San francisco | 4 | 0.0167 |
| San Rafael | 4 | 0.0167 |
| San Ramon | 4 | 0.0167 |
| Sandy | 4 | 0.0167 |
| Saratoga | 4 | 0.0167 |
| Schenectady | 4 | 0.0167 |
| South Jordan | 4 | 0.0167 |
| St. John's | 4 | 0.0167 |
| Stratford | 4 | 0.0167 |
| Terre Haute | 4 | 0.0167 |
| Thousand Oaks | 4 | 0.0167 |
| toronto | 4 | 0.0167 |
| Torrance | 4 | 0.0167 |
| Tualatin | 4 | 0.0167 |
| Tupelo | 4 | 0.0167 |
| Tustin | 4 | 0.0167 |
| Tysons | 4 | 0.0167 |
| UK | 4 | 0.0167 |
| Upstate NY | 4 | 0.0167 |
| Urbana | 4 | 0.0167 |
| Vancouver, British Columbia | 4 | 0.0167 |
| Warner Robins | 4 | 0.0167 |
| Warren | 4 | 0.0167 |
| washington dc | 4 | 0.0167 |
| Waukesha | 4 | 0.0167 |
| Waynesboro | 4 | 0.0167 |
| West Des Moines | 4 | 0.0167 |
| Westlake | 4 | 0.0167 |
| Westminster | 4 | 0.0167 |
| WFH | 4 | 0.0167 |
| Winston Salem | 4 | 0.0167 |
| Work from home | 4 | 0.0167 |
| Yuma | 4 | 0.0167 |
| Abbotsford | 3 | 0.0125 |
| Adelaide, South Australia | 3 | 0.0125 |
| Albany, NY | 3 | 0.0125 |
| Alberta | 3 | 0.0125 |
| Alexandria, VA | 3 | 0.0125 |
| Anderson | 3 | 0.0125 |
| Ankeny | 3 | 0.0125 |
| Anonymous | 3 | 0.0125 |
| Antwerp | 3 | 0.0125 |
| Arlington Heights | 3 | 0.0125 |
| Arlington, VA | 3 | 0.0125 |
| Arvada | 3 | 0.0125 |
| Ashburn | 3 | 0.0125 |
| atlanta | 3 | 0.0125 |
| Atlantic City | 3 | 0.0125 |
| Attleboro | 3 | 0.0125 |
| Austin, TX | 3 | 0.0125 |
| Bakersfield | 3 | 0.0125 |
| Barnstable | 3 | 0.0125 |
| BC | 3 | 0.0125 |
| Beachwood | 3 | 0.0125 |
| Bemidji | 3 | 0.0125 |
| Bend | 3 | 0.0125 |
| Bennington | 3 | 0.0125 |
| Bergen County | 3 | 0.0125 |
| Birmingham, AL | 3 | 0.0125 |
| Bismarck | 3 | 0.0125 |
| Bloomfield Hills | 3 | 0.0125 |
| Bonn | 3 | 0.0125 |
| Bradford | 3 | 0.0125 |
| Bridgeport | 3 | 0.0125 |
| Bridgewater | 3 | 0.0125 |
| British Columbia | 3 | 0.0125 |
| Brockville | 3 | 0.0125 |
| Brookline | 3 | 0.0125 |
| Brunswick | 3 | 0.0125 |
| Camden | 3 | 0.0125 |
| Cape Girardeau | 3 | 0.0125 |
| Carbondale | 3 | 0.0125 |
| Carlisle | 3 | 0.0125 |
| Central PA | 3 | 0.0125 |
| Chanhassen | 3 | 0.0125 |
| charlotte | 3 | 0.0125 |
| Charlottetown | 3 | 0.0125 |
| Cheltenham | 3 | 0.0125 |
| Chesapeake | 3 | 0.0125 |
| Cheshire | 3 | 0.0125 |
| Cheyenne | 3 | 0.0125 |
| Chilliwack | 3 | 0.0125 |
| Cincinnati, OH | 3 | 0.0125 |
| City | 3 | 0.0125 |
| Clinton | 3 | 0.0125 |
| Colorado springs | 3 | 0.0125 |
| Columbus, OH | 3 | 0.0125 |
| Council Bluffs | 3 | 0.0125 |
| Cumming | 3 | 0.0125 |
| Dearborn | 3 | 0.0125 |
| Denver metro | 3 | 0.0125 |
| Denver Metro Area | 3 | 0.0125 |
| Denver, CO | 3 | 0.0125 |
| Douglasville | 3 | 0.0125 |
| Doylestown | 3 | 0.0125 |
| dublin | 3 | 0.0125 |
| Dulles | 3 | 0.0125 |
| East Hanover | 3 | 0.0125 |
| Elizabeth | 3 | 0.0125 |
| Eureka | 3 | 0.0125 |
| Fairfax County | 3 | 0.0125 |
| Fall River | 3 | 0.0125 |
| Fishers | 3 | 0.0125 |
| Fort Smith | 3 | 0.0125 |
| Fort wayne | 3 | 0.0125 |
| Fredericton | 3 | 0.0125 |
| Freeport | 3 | 0.0125 |
| Fullerton | 3 | 0.0125 |
| Geelong | 3 | 0.0125 |
| Germany | 3 | 0.0125 |
| Glendale Heights | 3 | 0.0125 |
| Glens Falls | 3 | 0.0125 |
| Grand Junction | 3 | 0.0125 |
| Greencastle | 3 | 0.0125 |
| Greenwich | 3 | 0.0125 |
| Gulfport | 3 | 0.0125 |
| Hackensack | 3 | 0.0125 |
| Halifax, Nova Scotia | 3 | 0.0125 |
| Hammond | 3 | 0.0125 |
| Hampton Roads | 3 | 0.0125 |
| hartford | 3 | 0.0125 |
| Hatfield | 3 | 0.0125 |
| Hattiesburg | 3 | 0.0125 |
| Hendersonville | 3 | 0.0125 |
| Hickory | 3 | 0.0125 |
| Hudson | 3 | 0.0125 |
| Hull | 3 | 0.0125 |
| Itasca | 3 | 0.0125 |
| Jefferson City | 3 | 0.0125 |
| Jeffersonville | 3 | 0.0125 |
| Johnston | 3 | 0.0125 |
| Joliet | 3 | 0.0125 |
| Kingsport | 3 | 0.0125 |
| Kitchener, Ontario | 3 | 0.0125 |
| LA | 3 | 0.0125 |
| La Grande | 3 | 0.0125 |
| Lake Forest | 3 | 0.0125 |
| Lansing, MI | 3 | 0.0125 |
| Largo | 3 | 0.0125 |
| Leeds, UK | 3 | 0.0125 |
| Lemont | 3 | 0.0125 |
| London, Ontario | 3 | 0.0125 |
| Loveland | 3 | 0.0125 |
| Lubbock | 3 | 0.0125 |
| Lynnwood | 3 | 0.0125 |
| Mahwah | 3 | 0.0125 |
| Malvern | 3 | 0.0125 |
| Many | 3 | 0.0125 |
| Maplewood | 3 | 0.0125 |
| Marquette | 3 | 0.0125 |
| Martinsburg | 3 | 0.0125 |
| Mechanicsburg | 3 | 0.0125 |
| Medina | 3 | 0.0125 |
| Melville | 3 | 0.0125 |
| Menomonee Falls | 3 | 0.0125 |
| Merced | 3 | 0.0125 |
| Metro Atlanta | 3 | 0.0125 |
| Middleton | 3 | 0.0125 |
| Minneapolis-St. Paul | 3 | 0.0125 |
| Minneapolis/St Paul | 3 | 0.0125 |
| Monroe | 3 | 0.0125 |
| Montvale | 3 | 0.0125 |
| Mount Laurel | 3 | 0.0125 |
| Mount Vernon | 3 | 0.0125 |
| Naples | 3 | 0.0125 |
| Nassau | 3 | 0.0125 |
| New Castle | 3 | 0.0125 |
| Newburyport | 3 | 0.0125 |
| No | 3 | 0.0125 |
| Normal | 3 | 0.0125 |
| North Andover | 3 | 0.0125 |
| Northern Ontario | 3 | 0.0125 |
| Northern VA | 3 | 0.0125 |
| Novato | 3 | 0.0125 |
| NY | 3 | 0.0125 |
| Oak Brook | 3 | 0.0125 |
| Olney | 3 | 0.0125 |
| Oshkosh | 3 | 0.0125 |
| Owings Mills | 3 | 0.0125 |
| Palm Springs | 3 | 0.0125 |
| Paso Robles | 3 | 0.0125 |
| Perrysburg | 3 | 0.0125 |
| Petaluma | 3 | 0.0125 |
| Peterborough | 3 | 0.0125 |
| Piscataway | 3 | 0.0125 |
| pittsburgh | 3 | 0.0125 |
| Plattsburgh | 3 | 0.0125 |
| Plymouth Meeting | 3 | 0.0125 |
| Portland, Oregon | 3 | 0.0125 |
| Prince George | 3 | 0.0125 |
| Pueblo | 3 | 0.0125 |
| Quantico | 3 | 0.0125 |
| Quebec City | 3 | 0.0125 |
| Queens | 3 | 0.0125 |
| Racine | 3 | 0.0125 |
| Red Bank | 3 | 0.0125 |
| Redlands | 3 | 0.0125 |
| Remote worker | 3 | 0.0125 |
| Riverdale | 3 | 0.0125 |
| Rochester Hills | 3 | 0.0125 |
| Rochester, NY | 3 | 0.0125 |
| Rogers | 3 | 0.0125 |
| Rosslyn | 3 | 0.0125 |
| Roswell | 3 | 0.0125 |
| Rotterdam | 3 | 0.0125 |
| San antonio | 3 | 0.0125 |
| San diego | 3 | 0.0125 |
| Saratoga Springs | 3 | 0.0125 |
| Sheboygan | 3 | 0.0125 |
| Sierra Vista | 3 | 0.0125 |
| Silverdale | 3 | 0.0125 |
| Singapore | 3 | 0.0125 |
| Sioux falls | 3 | 0.0125 |
| Solon | 3 | 0.0125 |
| South Hadley | 3 | 0.0125 |
| St Paul, MN | 3 | 0.0125 |
| St. Cloud | 3 | 0.0125 |
| Stanford | 3 | 0.0125 |
| Starkville | 3 | 0.0125 |
| Staunton | 3 | 0.0125 |
| Sterling | 3 | 0.0125 |
| Stevens Point | 3 | 0.0125 |
| Stockholm | 3 | 0.0125 |
| Suburban Chicago | 3 | 0.0125 |
| Sunderland | 3 | 0.0125 |
| Suwanee | 3 | 0.0125 |
| Swindon | 3 | 0.0125 |
| Tarrytown | 3 | 0.0125 |
| Tel Aviv | 3 | 0.0125 |
| The Hague | 3 | 0.0125 |
| Thunder Bay | 3 | 0.0125 |
| Tigard | 3 | 0.0125 |
| Truro | 3 | 0.0125 |
| Valencia | 3 | 0.0125 |
| Valparaiso | 3 | 0.0125 |
| vancouver | 3 | 0.0125 |
| Vancouver Island | 3 | 0.0125 |
| Virginia | 3 | 0.0125 |
| Virtual | 3 | 0.0125 |
| Wakefield | 3 | 0.0125 |
| Waterford | 3 | 0.0125 |
| Waterville | 3 | 0.0125 |
| Weehawken | 3 | 0.0125 |
| Wells | 3 | 0.0125 |
| Westbrook | 3 | 0.0125 |
| Westchester | 3 | 0.0125 |
| Westfield | 3 | 0.0125 |
| Westwood | 3 | 0.0125 |
| Wheeling | 3 | 0.0125 |
| Whitehorse | 3 | 0.0125 |
| Winchester | 3 | 0.0125 |
| Woodinville | 3 | 0.0125 |
| -- | 2 | 0.00836 |
| . | 2 | 0.00836 |
| A Coruña | 2 | 0.00836 |
| Abilene | 2 | 0.00836 |
| Accra | 2 | 0.00836 |
| Addison | 2 | 0.00836 |
| Agawam | 2 | 0.00836 |
| Alamogordo | 2 | 0.00836 |
| albuquerque | 2 | 0.00836 |
| Allen | 2 | 0.00836 |
| Alton | 2 | 0.00836 |
| Ambler | 2 | 0.00836 |
| amsterdam | 2 | 0.00836 |
| anchorage | 2 | 0.00836 |
| ann arbor | 2 | 0.00836 |
| Annandale | 2 | 0.00836 |
| Apache Junction | 2 | 0.00836 |
| Apex | 2 | 0.00836 |
| Apple Valley | 2 | 0.00836 |
| arlington | 2 | 0.00836 |
| Arroyo Grande | 2 | 0.00836 |
| Aspen | 2 | 0.00836 |
| ATLANTA | 2 | 0.00836 |
| Atlanta, GA | 2 | 0.00836 |
| austin | 2 | 0.00836 |
| Ayer | 2 | 0.00836 |
| Baltimore Area | 2 | 0.00836 |
| Baltimore County | 2 | 0.00836 |
| Baltimore, MD | 2 | 0.00836 |
| Bangalore | 2 | 0.00836 |
| Bangkok | 2 | 0.00836 |
| Barrie | 2 | 0.00836 |
| Barrington | 2 | 0.00836 |
| Basingstoke | 2 | 0.00836 |
| Batesville | 2 | 0.00836 |
| Bay area | 2 | 0.00836 |
| Belmont | 2 | 0.00836 |
| Berwyn | 2 | 0.00836 |
| Bethesda MD | 2 | 0.00836 |
| Beverly Hills | 2 | 0.00836 |
| Birmingham UK | 2 | 0.00836 |
| Birmingham, England | 2 | 0.00836 |
| Birmingham, UK | 2 | 0.00836 |
| Bishop | 2 | 0.00836 |
| Bloomfield | 2 | 0.00836 |
| Blue Bell | 2 | 0.00836 |
| Bluffton | 2 | 0.00836 |
| BOSTON | 2 | 0.00836 |
| Boston suburbs | 2 | 0.00836 |
| Bowmanville | 2 | 0.00836 |
| Boynton Beach | 2 | 0.00836 |
| Braintree | 2 | 0.00836 |
| Brampton | 2 | 0.00836 |
| Branford | 2 | 0.00836 |
| Brantford | 2 | 0.00836 |
| Brattleboro | 2 | 0.00836 |
| Braunschweig | 2 | 0.00836 |
| Brewster | 2 | 0.00836 |
| Brookfield | 2 | 0.00836 |
| Buckinghamshire | 2 | 0.00836 |
| buffalo | 2 | 0.00836 |
| Burlingame | 2 | 0.00836 |
| Byron Bay | 2 | 0.00836 |
| Calgary, AB | 2 | 0.00836 |
| Calgary, Alberta | 2 | 0.00836 |
| Cambridge, ON | 2 | 0.00836 |
| Cambridge, UK | 2 | 0.00836 |
| Camp hill | 2 | 0.00836 |
| Campbell | 2 | 0.00836 |
| Cape Town | 2 | 0.00836 |
| Carmel | 2 | 0.00836 |
| Carpinteria | 2 | 0.00836 |
| Carson | 2 | 0.00836 |
| Cary, NC | 2 | 0.00836 |
| Casper | 2 | 0.00836 |
| Centennial | 2 | 0.00836 |
| Central IL | 2 | 0.00836 |
| Central Virginia | 2 | 0.00836 |
| Chestnut Hill | 2 | 0.00836 |
| Chevy Chase | 2 | 0.00836 |
| Chicago (suburbs) | 2 | 0.00836 |
| Chicopee | 2 | 0.00836 |
| Clarksburg | 2 | 0.00836 |
| Cleveland, Ohio | 2 | 0.00836 |
| Cloquet | 2 | 0.00836 |
| Cluj-Napoca | 2 | 0.00836 |
| Coeur d'Alene | 2 | 0.00836 |
| Collegeville | 2 | 0.00836 |
| Collingswood | 2 | 0.00836 |
| Columbia, SC | 2 | 0.00836 |
| Conyers | 2 | 0.00836 |
| Coral Gables | 2 | 0.00836 |
| Coralville | 2 | 0.00836 |
| Covington | 2 | 0.00836 |
| Crawley | 2 | 0.00836 |
| Crossville | 2 | 0.00836 |
| Croydon | 2 | 0.00836 |
| Culver City | 2 | 0.00836 |
| Curitiba | 2 | 0.00836 |
| dallas | 2 | 0.00836 |
| Dalton | 2 | 0.00836 |
| Danvers | 2 | 0.00836 |
| Danville | 2 | 0.00836 |
| dc | 2 | 0.00836 |
| DC metro area | 2 | 0.00836 |
| Decline | 2 | 0.00836 |
| Decline to answer | 2 | 0.00836 |
| Deerfield Beach | 2 | 0.00836 |
| DeKalb | 2 | 0.00836 |
| Delaware | 2 | 0.00836 |
| Delft | 2 | 0.00836 |
| Denver CO | 2 | 0.00836 |
| Des Moines metro | 2 | 0.00836 |
| Detroit area | 2 | 0.00836 |
| Detroit metro | 2 | 0.00836 |
| Detroit Metro | 2 | 0.00836 |
| Detroit, MI | 2 | 0.00836 |
| Devon | 2 | 0.00836 |
| Dunedin | 2 | 0.00836 |
| Durango | 2 | 0.00836 |
| East Hartford | 2 | 0.00836 |
| East Moline | 2 | 0.00836 |
| East Windsor | 2 | 0.00836 |
| Eastern Iowa | 2 | 0.00836 |
| Eastern Washington | 2 | 0.00836 |
| Edison | 2 | 0.00836 |
| edmonton | 2 | 0.00836 |
| Effingham | 2 | 0.00836 |
| Elkhart | 2 | 0.00836 |
| Elkridge | 2 | 0.00836 |
| Ellicott City | 2 | 0.00836 |
| Elmira | 2 | 0.00836 |
| Elyria | 2 | 0.00836 |
| Emeryville | 2 | 0.00836 |
| Essex | 2 | 0.00836 |
| Eufaula | 2 | 0.00836 |
| Evanston, IL | 2 | 0.00836 |
| Fair Lawn | 2 | 0.00836 |
| Fallon | 2 | 0.00836 |
| Falls Church, VA | 2 | 0.00836 |
| Falmouth | 2 | 0.00836 |
| Farmington | 2 | 0.00836 |
| Flemington | 2 | 0.00836 |
| Fort Bragg | 2 | 0.00836 |
| Fort McMurray | 2 | 0.00836 |
| Fort Washington | 2 | 0.00836 |
| Framingham, MA | 2 | 0.00836 |
| Fredonia | 2 | 0.00836 |
| From home | 2 | 0.00836 |
| Ft Lauderdale | 2 | 0.00836 |
| Ft Meade | 2 | 0.00836 |
| Fully Remote | 2 | 0.00836 |
| Fulton | 2 | 0.00836 |
| Galveston | 2 | 0.00836 |
| Garden City | 2 | 0.00836 |
| Garden grove | 2 | 0.00836 |
| Garyville | 2 | 0.00836 |
| Gent | 2 | 0.00836 |
| Gold Coast | 2 | 0.00836 |
| Grand rapids | 2 | 0.00836 |
| Grapevine | 2 | 0.00836 |
| Greater Boston | 2 | 0.00836 |
| Greater Boston area | 2 | 0.00836 |
| Greater Boston Area | 2 | 0.00836 |
| Greater Chicago Area | 2 | 0.00836 |
| Greater Cleveland | 2 | 0.00836 |
| Greater London | 2 | 0.00836 |
| Greater Philadelphia area | 2 | 0.00836 |
| Greater Philly | 2 | 0.00836 |
| Grinnell | 2 | 0.00836 |
| Groton | 2 | 0.00836 |
| Grove City | 2 | 0.00836 |
| Guildford | 2 | 0.00836 |
| Hagerstown | 2 | 0.00836 |
| Halifax, NS | 2 | 0.00836 |
| Hamden | 2 | 0.00836 |
| Hamilton, ON | 2 | 0.00836 |
| Havre de Grace | 2 | 0.00836 |
| Hawthorne | 2 | 0.00836 |
| Hayward | 2 | 0.00836 |
| Heilbronn | 2 | 0.00836 |
| Herne | 2 | 0.00836 |
| High Point | 2 | 0.00836 |
| Hillsborough | 2 | 0.00836 |
| Hilo | 2 | 0.00836 |
| Hollywood | 2 | 0.00836 |
| Houston area | 2 | 0.00836 |
| Houston, TX | 2 | 0.00836 |
| Huddersfield | 2 | 0.00836 |
| Hunt Valley | 2 | 0.00836 |
| Huntingdon | 2 | 0.00836 |
| Huntington | 2 | 0.00836 |
| Ipswich | 2 | 0.00836 |
| Ireland | 2 | 0.00836 |
| Iselin | 2 | 0.00836 |
| Jamestown | 2 | 0.00836 |
| Jefferson | 2 | 0.00836 |
| Johnson City | 2 | 0.00836 |
| Jupiter | 2 | 0.00836 |
| Kansas city | 2 | 0.00836 |
| Kansas City area | 2 | 0.00836 |
| Karachi | 2 | 0.00836 |
| Kearney | 2 | 0.00836 |
| Kelowna, BC | 2 | 0.00836 |
| Kennebunk | 2 | 0.00836 |
| Kennesaw | 2 | 0.00836 |
| Kensington | 2 | 0.00836 |
| Kirtland | 2 | 0.00836 |
| Kitchener, ON | 2 | 0.00836 |
| La Vergne | 2 | 0.00836 |
| Lake Bluff | 2 | 0.00836 |
| Lake Jackson | 2 | 0.00836 |
| Lake Mary | 2 | 0.00836 |
| Lake Zurich | 2 | 0.00836 |
| Lansdale | 2 | 0.00836 |
| Larkspur | 2 | 0.00836 |
| Launceston | 2 | 0.00836 |
| Lawrenceville, NJ | 2 | 0.00836 |
| Leamington Spa | 2 | 0.00836 |
| Lee's Summit | 2 | 0.00836 |
| Lehigh Valley | 2 | 0.00836 |
| Leiden | 2 | 0.00836 |
| Leominster | 2 | 0.00836 |
| Leuven | 2 | 0.00836 |
| Lewisville | 2 | 0.00836 |
| Lima | 2 | 0.00836 |
| Lititz | 2 | 0.00836 |
| Livermore | 2 | 0.00836 |
| Livingston | 2 | 0.00836 |
| Livonia | 2 | 0.00836 |
| Los Angeles County | 2 | 0.00836 |
| Los Angeles, CA | 2 | 0.00836 |
| Loughborough | 2 | 0.00836 |
| louisville | 2 | 0.00836 |
| Lower Mainland | 2 | 0.00836 |
| Luton | 2 | 0.00836 |
| Luxembourg | 2 | 0.00836 |
| Mackay | 2 | 0.00836 |
| Malone | 2 | 0.00836 |
| Manchester, UK | 2 | 0.00836 |
| Manila | 2 | 0.00836 |
| Maple Grove | 2 | 0.00836 |
| Markham | 2 | 0.00836 |
| Marlton | 2 | 0.00836 |
| Marshfield | 2 | 0.00836 |
| Maryland | 2 | 0.00836 |
| Marysville | 2 | 0.00836 |
| Maryville | 2 | 0.00836 |
| McDonough | 2 | 0.00836 |
| McLean, VA | 2 | 0.00836 |
| McMinnville | 2 | 0.00836 |
| Mechanicsville | 2 | 0.00836 |
| Media | 2 | 0.00836 |
| melbourne | 2 | 0.00836 |
| Merrimack | 2 | 0.00836 |
| Merritt Island | 2 | 0.00836 |
| Mesa | 2 | 0.00836 |
| Mexico City | 2 | 0.00836 |
| Miami Beach | 2 | 0.00836 |
| Milpitas | 2 | 0.00836 |
| milwaukee | 2 | 0.00836 |
| Milwaukee, WI | 2 | 0.00836 |
| minneapolis | 2 | 0.00836 |
| Minneapolis suburbs | 2 | 0.00836 |
| Minneapolis-St Paul | 2 | 0.00836 |
| Mishawaka | 2 | 0.00836 |
| Mission | 2 | 0.00836 |
| Monterey | 2 | 0.00836 |
| Montreal, QC | 2 | 0.00836 |
| Morgan Hill | 2 | 0.00836 |
| Morris | 2 | 0.00836 |
| Moscow | 2 | 0.00836 |
| Muncie | 2 | 0.00836 |
| Murray | 2 | 0.00836 |
| Nairobi | 2 | 0.00836 |
| Napa | 2 | 0.00836 |
| nashville | 2 | 0.00836 |
| Nauvoo | 2 | 0.00836 |
| Near LA | 2 | 0.00836 |
| Neenah | 2 | 0.00836 |
| Nevada | 2 | 0.00836 |
| New Albany | 2 | 0.00836 |
| New Britain | 2 | 0.00836 |
| New haven | 2 | 0.00836 |
| New Hope | 2 | 0.00836 |
| New Jersey | 2 | 0.00836 |
| New London | 2 | 0.00836 |
| New Paltz | 2 | 0.00836 |
| New Plymouth | 2 | 0.00836 |
| New Ulm | 2 | 0.00836 |
| Newburgh | 2 | 0.00836 |
| Newcastle NSW | 2 | 0.00836 |
| Newcastle Upon Tyne | 2 | 0.00836 |
| Newcastle-upon-Tyne | 2 | 0.00836 |
| Newnan | 2 | 0.00836 |
| Newtown | 2 | 0.00836 |
| Newtown Square | 2 | 0.00836 |
| None | 2 | 0.00836 |
| Norcross | 2 | 0.00836 |
| North Bay | 2 | 0.00836 |
| North Carolina | 2 | 0.00836 |
| North East | 2 | 0.00836 |
| North Haven | 2 | 0.00836 |
| North of Boston | 2 | 0.00836 |
| North Port | 2 | 0.00836 |
| Northeast Ohio | 2 | 0.00836 |
| Nova Scotia | 2 | 0.00836 |
| Nuremberg | 2 | 0.00836 |
| Nürnberg | 2 | 0.00836 |
| O | 2 | 0.00836 |
| O'Fallon | 2 | 0.00836 |
| Oakville | 2 | 0.00836 |
| Ocean Springs | 2 | 0.00836 |
| Oceanside | 2 | 0.00836 |
| Okinawa | 2 | 0.00836 |
| Omaha, NE | 2 | 0.00836 |
| Opelika | 2 | 0.00836 |
| Orange County Area | 2 | 0.00836 |
| orlando | 2 | 0.00836 |
| Other | 2 | 0.00836 |
| ottawa | 2 | 0.00836 |
| Owensboro | 2 | 0.00836 |
| Oxfordshire | 2 | 0.00836 |
| Palmdale | 2 | 0.00836 |
| Palo alto | 2 | 0.00836 |
| Paramus | 2 | 0.00836 |
| Paterson | 2 | 0.00836 |
| Pelham | 2 | 0.00836 |
| Pembroke | 2 | 0.00836 |
| Peoria Illinois | 2 | 0.00836 |
| philadelphia | 2 | 0.00836 |
| Philadelphia suburb | 2 | 0.00836 |
| Phx | 2 | 0.00836 |
| Pittsboro | 2 | 0.00836 |
| Pittsburg | 2 | 0.00836 |
| PIttsburgh | 2 | 0.00836 |
| Pittsburgh, PA | 2 | 0.00836 |
| Pittsfield | 2 | 0.00836 |
| Plantation | 2 | 0.00836 |
| Platteville | 2 | 0.00836 |
| Portage | 2 | 0.00836 |
| Portland area | 2 | 0.00836 |
| Potomac | 2 | 0.00836 |
| Potsdam | 2 | 0.00836 |
| Pottstown | 2 | 0.00836 |
| Prefer not to disclose | 2 | 0.00836 |
| Prefer not to respond | 2 | 0.00836 |
| Prescott | 2 | 0.00836 |
| Preston | 2 | 0.00836 |
| princeton | 2 | 0.00836 |
| Province | 2 | 0.00836 |
| Provincetown | 2 | 0.00836 |
| Purchase | 2 | 0.00836 |
| Quakertown | 2 | 0.00836 |
| Queensland | 2 | 0.00836 |
| Raleigh-Durham | 2 | 0.00836 |
| Raleigh, NC | 2 | 0.00836 |
| Raymond | 2 | 0.00836 |
| Reading, UK | 2 | 0.00836 |
| Reims | 2 | 0.00836 |
| Remotely | 2 | 0.00836 |
| Remotely from MD | 2 | 0.00836 |
| Research Triangle | 2 | 0.00836 |
| Richmond, VA | 2 | 0.00836 |
| Ridgefield | 2 | 0.00836 |
| Riga | 2 | 0.00836 |
| Ripon | 2 | 0.00836 |
| Riverview | 2 | 0.00836 |
| Rockland | 2 | 0.00836 |
| Rockville Centre | 2 | 0.00836 |
| Rolla | 2 | 0.00836 |
| Rolling Meadows | 2 | 0.00836 |
| Rome | 2 | 0.00836 |
| Ronkonkoma | 2 | 0.00836 |
| Rosemont | 2 | 0.00836 |
| Rosemount | 2 | 0.00836 |
| Rural town | 2 | 0.00836 |
| Sacramento, CA | 2 | 0.00836 |
| Saint Cloud | 2 | 0.00836 |
| Saint paul | 2 | 0.00836 |
| Saint Petersburg | 2 | 0.00836 |
| Salem, OR | 2 | 0.00836 |
| Salinas | 2 | 0.00836 |
| Saline | 2 | 0.00836 |
| Salt Lake | 2 | 0.00836 |
| San Bernardino | 2 | 0.00836 |
| San Francisco Bay area | 2 | 0.00836 |
| SAN JOSE | 2 | 0.00836 |
| San Jose, CA | 2 | 0.00836 |
| San Juan | 2 | 0.00836 |
| Sandpoint | 2 | 0.00836 |
| Saranac Lake | 2 | 0.00836 |
| Seabrook | 2 | 0.00836 |
| Seattle area | 2 | 0.00836 |
| Seattle Area | 2 | 0.00836 |
| Simi Valley | 2 | 0.00836 |
| Slc | 2 | 0.00836 |
| Sligo | 2 | 0.00836 |
| small town | 2 | 0.00836 |
| Sofia | 2 | 0.00836 |
| South Burlington | 2 | 0.00836 |
| South East England | 2 | 0.00836 |
| South Jersey | 2 | 0.00836 |
| South Lake Tahoe | 2 | 0.00836 |
| South Sioux City | 2 | 0.00836 |
| Southern California | 2 | 0.00836 |
| St Albans | 2 | 0.00836 |
| St louis | 2 | 0.00836 |
| st paul | 2 | 0.00836 |
| St. Marys | 2 | 0.00836 |
| Stillwater | 2 | 0.00836 |
| Storrs | 2 | 0.00836 |
| Stuttgart | 2 | 0.00836 |
| Suburb of Chicago | 2 | 0.00836 |
| Suburbs of Philadelphia | 2 | 0.00836 |
| Sudbury | 2 | 0.00836 |
| Suffolk | 2 | 0.00836 |
| Summit | 2 | 0.00836 |
| Sunrise | 2 | 0.00836 |
| Sussex | 2 | 0.00836 |
| Swansea | 2 | 0.00836 |
| sydney | 2 | 0.00836 |
| Sydney, Australia | 2 | 0.00836 |
| Tallinn | 2 | 0.00836 |
| Texarkana | 2 | 0.00836 |
| The Woodlands | 2 | 0.00836 |
| Thessaloniki | 2 | 0.00836 |
| Titusville | 2 | 0.00836 |
| Tokyo | 2 | 0.00836 |
| Toronto, ON | 2 | 0.00836 |
| Torrington | 2 | 0.00836 |
| Townsville | 2 | 0.00836 |
| Tracy | 2 | 0.00836 |
| Triangle | 2 | 0.00836 |
| Truckee | 2 | 0.00836 |
| Trumbull | 2 | 0.00836 |
| Tumwater | 2 | 0.00836 |
| Tunbridge Wells | 2 | 0.00836 |
| Twin Cities Metro | 2 | 0.00836 |
| Twin Cities suburbs | 2 | 0.00836 |
| Twin Falls | 2 | 0.00836 |
| Ulm | 2 | 0.00836 |
| Undisclosed | 2 | 0.00836 |
| Upper Marlboro | 2 | 0.00836 |
| Utah County | 2 | 0.00836 |
| Vacaville | 2 | 0.00836 |
| Valdosta | 2 | 0.00836 |
| Vallejo | 2 | 0.00836 |
| Vancouver BC | 2 | 0.00836 |
| Vernon | 2 | 0.00836 |
| Vestal | 2 | 0.00836 |
| Vicksburg | 2 | 0.00836 |
| VICTORIA | 2 | 0.00836 |
| Victoria, BC | 2 | 0.00836 |
| Vilnius | 2 | 0.00836 |
| Walla Walla | 2 | 0.00836 |
| Warminster | 2 | 0.00836 |
| Warrenville | 2 | 0.00836 |
| Warsaw | 2 | 0.00836 |
| Washington, D.C | 2 | 0.00836 |
| Waterloo, Ontario | 2 | 0.00836 |
| Wayne | 2 | 0.00836 |
| West Hartford | 2 | 0.00836 |
| West palm beach | 2 | 0.00836 |
| Weymouth | 2 | 0.00836 |
| Whippany | 2 | 0.00836 |
| Whitewater | 2 | 0.00836 |
| Wilkes Barre | 2 | 0.00836 |
| Windsor Locks | 2 | 0.00836 |
| Winnetka | 2 | 0.00836 |
| Winter Haven | 2 | 0.00836 |
| Wyomissing | 2 | 0.00836 |
| xxx | 2 | 0.00836 |
| Yellowknife | 2 | 0.00836 |
| Zwolle | 2 | 0.00836 |
| --- | 1 | 0.00418 |
| ----- | 1 | 0.00418 |
| “Large” Canadian prairie city. | 1 | 0.00418 |
| (Province of Alberta) | 1 | 0.00418 |
| (Rural) | 1 | 0.00418 |
| **Too small a sample size, can't share | 1 | 0.00418 |
| / | 1 | 0.00418 |
| 0000 | 1 | 0.00418 |
| 12043 | 1 | 0.00418 |
| 46901 | 1 | 0.00418 |
| 60k Small City | 1 | 0.00418 |
| A city in the UK | 1 | 0.00418 |
| A city small enough to not answer this question | 1 | 0.00418 |
| A large city | 1 | 0.00418 |
| A major Canadian city | 1 | 0.00418 |
| A major one | 1 | 0.00418 |
| A small city | 1 | 0.00418 |
| a small village in Okinawa prefecture (sorry, it's too identifying) | 1 | 0.00418 |
| A very large city | 1 | 0.00418 |
| Aachen | 1 | 0.00418 |
| Aberdeen Proving Ground | 1 | 0.00418 |
| Aberdeenshire | 1 | 0.00418 |
| Abidjan | 1 | 0.00418 |
| Abington | 1 | 0.00418 |
| Abington, PA | 1 | 0.00418 |
| Acton, MA | 1 | 0.00418 |
| Affluent Chicago suburb | 1 | 0.00418 |
| Ainsworth | 1 | 0.00418 |
| Akron, Ohio | 1 | 0.00418 |
| Albany region | 1 | 0.00418 |
| Albany-Schenectady-Troy Metro Area | 1 | 0.00418 |
| Albemarle | 1 | 0.00418 |
| Albuquerqur | 1 | 0.00418 |
| Aldie | 1 | 0.00418 |
| Aldie, VA | 1 | 0.00418 |
| Alexandria VA | 1 | 0.00418 |
| Alexandria, LA | 1 | 0.00418 |
| Alexandrira | 1 | 0.00418 |
| Algonac | 1 | 0.00418 |
| Alice Springs | 1 | 0.00418 |
| all over N CA | 1 | 0.00418 |
| Allegan | 1 | 0.00418 |
| Allen Park | 1 | 0.00418 |
| Allen, TX | 1 | 0.00418 |
| Allen/Plano | 1 | 0.00418 |
| Allendale, MI | 1 | 0.00418 |
| Alpharetta, GA | 1 | 0.00418 |
| Alsace (remote to Paris) | 1 | 0.00418 |
| Altadena | 1 | 0.00418 |
| Altamonte Springs | 1 | 0.00418 |
| Altoona | 1 | 0.00418 |
| Altus | 1 | 0.00418 |
| Alvarado | 1 | 0.00418 |
| American Fork | 1 | 0.00418 |
| Amesbury | 1 | 0.00418 |
| Amherst, MA | 1 | 0.00418 |
| Amityville | 1 | 0.00418 |
| Amman | 1 | 0.00418 |
| Ammon | 1 | 0.00418 |
| Amory | 1 | 0.00418 |
| Amsterdsm | 1 | 0.00418 |
| An exact answer would out me | 1 | 0.00418 |
| Anaconda | 1 | 0.00418 |
| Anacortes | 1 | 0.00418 |
| Anaheim-Santa Ana-Irvine metro region | 1 | 0.00418 |
| Anchoragr | 1 | 0.00418 |
| Ann Arbor area | 1 | 0.00418 |
| Ann Arbor, Michigan | 1 | 0.00418 |
| annandale Va | 1 | 0.00418 |
| Annapolis Junction | 1 | 0.00418 |
| Annapolis Valley | 1 | 0.00418 |
| Anon | 1 | 0.00418 |
| anonymous | 1 | 0.00418 |
| Arcadia | 1 | 0.00418 |
| Arden | 1 | 0.00418 |
| Ardmore | 1 | 0.00418 |
| Ardrossan | 1 | 0.00418 |
| Arkadelphia | 1 | 0.00418 |
| Arles | 1 | 0.00418 |
| ARLINGTON | 1 | 0.00418 |
| arlington, va | 1 | 0.00418 |
| Arlington, VA (Washington D.C. metro area) | 1 | 0.00418 |
| Arnold | 1 | 0.00418 |
| Asbury Park | 1 | 0.00418 |
| Asheboro | 1 | 0.00418 |
| Asheville, NC | 1 | 0.00418 |
| Ashford | 1 | 0.00418 |
| Asmara | 1 | 0.00418 |
| aston | 1 | 0.00418 |
| at home | 1 | 0.00418 |
| Atascadero | 1 | 0.00418 |
| Athlone | 1 | 0.00418 |
| Athol | 1 | 0.00418 |
| AtlantA | 1 | 0.00418 |
| Atlanta Metro | 1 | 0.00418 |
| Atlanta suburb | 1 | 0.00418 |
| Atlanta, Georgia | 1 | 0.00418 |
| Atlanta/Decatur | 1 | 0.00418 |
| Auburn Hills | 1 | 0.00418 |
| auckland | 1 | 0.00418 |
| Augusta - remote | 1 | 0.00418 |
| Augusta IL | 1 | 0.00418 |
| aurora | 1 | 0.00418 |
| Austell | 1 | 0.00418 |
| Austin metro area | 1 | 0.00418 |
| Austin Metro Area | 1 | 0.00418 |
| Australia not Sydney | 1 | 0.00418 |
| Avon Lake | 1 | 0.00418 |
| Aylesbury | 1 | 0.00418 |
| Badajoz | 1 | 0.00418 |
| Baden-Württemberg | 1 | 0.00418 |
| Bagdad | 1 | 0.00418 |
| Bainbridge Island | 1 | 0.00418 |
| Ball Ground | 1 | 0.00418 |
| Ballarat | 1 | 0.00418 |
| Ballston Spa | 1 | 0.00418 |
| BALTIMORE | 1 | 0.00418 |
| baltimore metro | 1 | 0.00418 |
| Baltimore metro region, MD | 1 | 0.00418 |
| Baltimore suburb | 1 | 0.00418 |
| Baltimore, Maryland | 1 | 0.00418 |
| Bangot | 1 | 0.00418 |
| Bannockburn | 1 | 0.00418 |
| Bardstown, KY. Store is located in Frankfort | 1 | 0.00418 |
| Barrie Ontario | 1 | 0.00418 |
| Bartlesville | 1 | 0.00418 |
| Bartlett | 1 | 0.00418 |
| barxelona | 1 | 0.00418 |
| Based on current client project | 1 | 0.00418 |
| Basel | 1 | 0.00418 |
| BASEL | 1 | 0.00418 |
| Basking Ridge | 1 | 0.00418 |
| Bathesda | 1 | 0.00418 |
| Bay Area / Palo Alto | 1 | 0.00418 |
| Bay Area, CA | 1 | 0.00418 |
| Bay of Plenty | 1 | 0.00418 |
| Bay St. Louis | 1 | 0.00418 |
| Beacon | 1 | 0.00418 |
| Beaufort | 1 | 0.00418 |
| Beaverton, OR (Portland suburb) | 1 | 0.00418 |
| Bedford, NH | 1 | 0.00418 |
| Bedfordshire | 1 | 0.00418 |
| Bedminster | 1 | 0.00418 |
| Beijing | 1 | 0.00418 |
| BELFAST | 1 | 0.00418 |
| Belfast - for context, our organization has country-based payscales, not city/state | 1 | 0.00418 |
| Belleville | 1 | 0.00418 |
| Bellevue, WA | 1 | 0.00418 |
| Bellevue, wa, usa | 1 | 0.00418 |
| Belmont, CA (SF Bay Area) | 1 | 0.00418 |
| Belo Horizonte | 1 | 0.00418 |
| Beltsville | 1 | 0.00418 |
| Bend, OR | 1 | 0.00418 |
| Bensenville | 1 | 0.00418 |
| Benton | 1 | 0.00418 |
| Benton Harbor | 1 | 0.00418 |
| Berea | 1 | 0.00418 |
| Bergen | 1 | 0.00418 |
| Berkley | 1 | 0.00418 |
| Berkley Heights | 1 | 0.00418 |
| berlin | 1 | 0.00418 |
| Berlin (remote for US clients) | 1 | 0.00418 |
| Berlin, Germany | 1 | 0.00418 |
| Berryville | 1 | 0.00418 |
| Berwick | 1 | 0.00418 |
| Bessemer | 1 | 0.00418 |
| Bethel | 1 | 0.00418 |
| Bethlehem, PA | 1 | 0.00418 |
| Bettendorf | 1 | 0.00418 |
| Beverly | 1 | 0.00418 |
| Beverly Hills, CA | 1 | 0.00418 |
| Biddeford | 1 | 0.00418 |
| BIG BEAR CITY | 1 | 0.00418 |
| Bigfork | 1 | 0.00418 |
| bilbao | 1 | 0.00418 |
| Billingham | 1 | 0.00418 |
| Biloxi | 1 | 0.00418 |
| Birm | 1 | 0.00418 |
| Blackpool | 1 | 0.00418 |
| Bloomington, IL | 1 | 0.00418 |
| Bluffdale | 1 | 0.00418 |
| Boardman | 1 | 0.00418 |
| Boca Chica | 1 | 0.00418 |
| Boerne | 1 | 0.00418 |
| Bolingbrook | 1 | 0.00418 |
| Bolton | 1 | 0.00418 |
| Bonita Springs | 1 | 0.00418 |
| Bordeaux | 1 | 0.00418 |
| Boron | 1 | 0.00418 |
| Boston (greater Boston metro area) | 1 | 0.00418 |
| Boston (Somerville specifically) | 1 | 0.00418 |
| Boston MA | 1 | 0.00418 |
| Boston Massachusetts | 1 | 0.00418 |
| Boston metro | 1 | 0.00418 |
| Boston Metro area | 1 | 0.00418 |
| Boston Metro Area | 1 | 0.00418 |
| Boston Suburb | 1 | 0.00418 |
| Boston, ma | 1 | 0.00418 |
| Boston(ish) | 1 | 0.00418 |
| Boston/Cambridge | 1 | 0.00418 |
| Boulder but we are remote | 1 | 0.00418 |
| Boulder City | 1 | 0.00418 |
| Boulder, CO | 1 | 0.00418 |
| Boxborough | 1 | 0.00418 |
| Boyertown | 1 | 0.00418 |
| Bozeman, MT | 1 | 0.00418 |
| Bracknell | 1 | 0.00418 |
| Braine | 1 | 0.00418 |
| Brambleton | 1 | 0.00418 |
| Brandon | 1 | 0.00418 |
| Bratislava | 1 | 0.00418 |
| Brea | 1 | 0.00418 |
| Bremen | 1 | 0.00418 |
| Bremerhaven | 1 | 0.00418 |
| Brest | 1 | 0.00418 |
| Brick | 1 | 0.00418 |
| Bridgend | 1 | 0.00418 |
| Bridgewater, NJ | 1 | 0.00418 |
| Brigham City | 1 | 0.00418 |
| brighton | 1 | 0.00418 |
| Brighton & Hove | 1 | 0.00418 |
| Brighton, MI | 1 | 0.00418 |
| Bristol UK | 1 | 0.00418 |
| Bristol, UK | 1 | 0.00418 |
| Bristow | 1 | 0.00418 |
| British Columbia (not a city, but I am rural) | 1 | 0.00418 |
| British Columbia (Province) | 1 | 0.00418 |
| Brittany | 1 | 0.00418 |
| brooklyn | 1 | 0.00418 |
| Brooklyn (WFH) | 1 | 0.00418 |
| Brooklyn Park | 1 | 0.00418 |
| Brooklyn Park, MN | 1 | 0.00418 |
| Brookston | 1 | 0.00418 |
| Brooksville | 1 | 0.00418 |
| Brookville | 1 | 0.00418 |
| Brownsville | 1 | 0.00418 |
| Brussel | 1 | 0.00418 |
| Bryan | 1 | 0.00418 |
| Bryan/College Station | 1 | 0.00418 |
| Bryn Mawr | 1 | 0.00418 |
| Bucharest | 1 | 0.00418 |
| Buckhannon | 1 | 0.00418 |
| Budapest | 1 | 0.00418 |
| Buenos Aires | 1 | 0.00418 |
| Buffalo Grove | 1 | 0.00418 |
| Buffalo, NY | 1 | 0.00418 |
| burbank | 1 | 0.00418 |
| Burgenland | 1 | 0.00418 |
| Burgos | 1 | 0.00418 |
| Burleson | 1 | 0.00418 |
| Burlington, MA | 1 | 0.00418 |
| Burly | 1 | 0.00418 |
| Burnaby | 1 | 0.00418 |
| Burnaby BC | 1 | 0.00418 |
| Burnsville, MN | 1 | 0.00418 |
| Butler | 1 | 0.00418 |
| BWG | 1 | 0.00418 |
| ca. 100k inhabitants | 1 | 0.00418 |
| Calgary AB | 1 | 0.00418 |
| Calgary Alberta | 1 | 0.00418 |
| Calgary Alberta Canada | 1 | 0.00418 |
| Calhoun, GA | 1 | 0.00418 |
| California | 1 | 0.00418 |
| Calumet City | 1 | 0.00418 |
| Camarillo | 1 | 0.00418 |
| Camarillo, CA | 1 | 0.00418 |
| Cambridge ,MA | 1 | 0.00418 |
| Cambridge (Boston metro area) | 1 | 0.00418 |
| Cambridge / Remote | 1 | 0.00418 |
| cambridge ma | 1 | 0.00418 |
| Cambridge MA | 1 | 0.00418 |
| Cambridge, Massachusetts | 1 | 0.00418 |
| Cambridge, Ontario | 1 | 0.00418 |
| Cambridge/Boston | 1 | 0.00418 |
| Cameron | 1 | 0.00418 |
| Camp Hill | 1 | 0.00418 |
| Camp Verde | 1 | 0.00418 |
| Campbell River | 1 | 0.00418 |
| can't say | 1 | 0.00418 |
| Can't say without being identifiable | 1 | 0.00418 |
| Can't say without losing my anonymity | 1 | 0.00418 |
| Can't share | 1 | 0.00418 |
| canberra | 1 | 0.00418 |
| Canbridge, UK | 1 | 0.00418 |
| Cannock | 1 | 0.00418 |
| canyon | 1 | 0.00418 |
| Cape Canaveral | 1 | 0.00418 |
| Capital Region | 1 | 0.00418 |
| Cardington | 1 | 0.00418 |
| Carlinville | 1 | 0.00418 |
| Carmel, Indiana | 1 | 0.00418 |
| Cartersville | 1 | 0.00418 |
| Cartersville GA | 1 | 0.00418 |
| Castlegar | 1 | 0.00418 |
| Caterham | 1 | 0.00418 |
| Catonsville | 1 | 0.00418 |
| CDA | 1 | 0.00418 |
| Cedar Grove, NJ | 1 | 0.00418 |
| cedar rapids | 1 | 0.00418 |
| Cedar rapids | 1 | 0.00418 |
| Cedarville | 1 | 0.00418 |
| Celina | 1 | 0.00418 |
| Center | 1 | 0.00418 |
| Centerville | 1 | 0.00418 |
| CENTRAL CITY | 1 | 0.00418 |
| Central Illinois | 1 | 0.00418 |
| Central Iowa | 1 | 0.00418 |
| Central Kentucky | 1 | 0.00418 |
| Central KY | 1 | 0.00418 |
| Central Maine | 1 | 0.00418 |
| Central MD | 1 | 0.00418 |
| central nj | 1 | 0.00418 |
| Central NJ | 1 | 0.00418 |
| Central NY | 1 | 0.00418 |
| Central Ohio | 1 | 0.00418 |
| Central Qld | 1 | 0.00418 |
| Central Valley | 1 | 0.00418 |
| Centralia | 1 | 0.00418 |
| Centreville | 1 | 0.00418 |
| Cerritos | 1 | 0.00418 |
| CFL | 1 | 0.00418 |
| Chambersburg | 1 | 0.00418 |
| Chamonix-Mont-Blanc | 1 | 0.00418 |
| Champaign Area | 1 | 0.00418 |
| Champaign Urbana | 1 | 0.00418 |
| Champaign-Urbana | 1 | 0.00418 |
| Champaign, IL | 1 | 0.00418 |
| Chantilly, VA | 1 | 0.00418 |
| Chapel hill | 1 | 0.00418 |
| Charles Town | 1 | 0.00418 |
| Charleston area | 1 | 0.00418 |
| Charlestown | 1 | 0.00418 |
| Charlotte area | 1 | 0.00418 |
| Charlotte metropolitan | 1 | 0.00418 |
| charlottesville | 1 | 0.00418 |
| Chaska | 1 | 0.00418 |
| Chatham | 1 | 0.00418 |
| Chatsworth | 1 | 0.00418 |
| Chehalis | 1 | 0.00418 |
| Chelsea | 1 | 0.00418 |
| Chemnitz | 1 | 0.00418 |
| Chennai | 1 | 0.00418 |
| Cheraw | 1 | 0.00418 |
| cherry hill | 1 | 0.00418 |
| Cherry hill | 1 | 0.00418 |
| Cherry Hill, NJ | 1 | 0.00418 |
| CHESAPEAKE | 1 | 0.00418 |
| Cheshire County | 1 | 0.00418 |
| Chester County | 1 | 0.00418 |
| Chester Springs, PA | 1 | 0.00418 |
| Chesterbrook | 1 | 0.00418 |
| Chesterfield, MI | 1 | 0.00418 |
| Chestertown | 1 | 0.00418 |
| Cheswick | 1 | 0.00418 |
| Cheyenne, Wy | 1 | 0.00418 |
| Chicacgo | 1 | 0.00418 |
| Chicago -remote position though | 1 | 0.00418 |
| Chicago (but distributed workplace) | 1 | 0.00418 |
| Chicago (remote) | 1 | 0.00418 |
| Chicago Area | 1 | 0.00418 |
| Chicago area mostly, but also the US and Canada | 1 | 0.00418 |
| Chicago but the company is remote | 1 | 0.00418 |
| Chicago metro | 1 | 0.00418 |
| Chicago Metro | 1 | 0.00418 |
| Chicago, IL (but I work remotely) | 1 | 0.00418 |
| Chicago/Deerfield (two offices) | 1 | 0.00418 |
| Chicagoland Area | 1 | 0.00418 |
| Chichester | 1 | 0.00418 |
| Chickasha | 1 | 0.00418 |
| Chillicothe | 1 | 0.00418 |
| Chilliwack, BC | 1 | 0.00418 |
| China Lake | 1 | 0.00418 |
| Chippenham | 1 | 0.00418 |
| Chirk | 1 | 0.00418 |
| choose not to answer | 1 | 0.00418 |
| Chula Vista | 1 | 0.00418 |
| Church Hill | 1 | 0.00418 |
| Cincinatti | 1 | 0.00418 |
| cincinnati | 1 | 0.00418 |
| Citrus Heights | 1 | 0.00418 |
| city in Middle Georgia | 1 | 0.00418 |
| City is too identifiable, state of NRW | 1 | 0.00418 |
| Ciudad de Panamá | 1 | 0.00418 |
| Clarendon Hills | 1 | 0.00418 |
| Clarksville | 1 | 0.00418 |
| Clawson | 1 | 0.00418 |
| Clayton | 1 | 0.00418 |
| Clearfield | 1 | 0.00418 |
| Clermont | 1 | 0.00418 |
| Cleveland Area | 1 | 0.00418 |
| Clifton | 1 | 0.00418 |
| Clifton Park | 1 | 0.00418 |
| Clinton Township | 1 | 0.00418 |
| Co Meath | 1 | 0.00418 |
| Cobourg | 1 | 0.00418 |
| Cocoa | 1 | 0.00418 |
| Coconut creek | 1 | 0.00418 |
| cody | 1 | 0.00418 |
| Coeur d’Alene | 1 | 0.00418 |
| Cold Spring, NY | 1 | 0.00418 |
| Collinsville | 1 | 0.00418 |
| Colombo | 1 | 0.00418 |
| Columbia Falls | 1 | 0.00418 |
| Columbia MD | 1 | 0.00418 |
| columbus | 1 | 0.00418 |
| Columbus, Boston | 1 | 0.00418 |
| Columbus, Indiana | 1 | 0.00418 |
| Colville | 1 | 0.00418 |
| Commack | 1 | 0.00418 |
| Company is based in Nashville, I work remotely from NYC | 1 | 0.00418 |
| Company is based in SF | 1 | 0.00418 |
| Company is in Glendale, California I am in Barrie, Ontario | 1 | 0.00418 |
| Company is in NYC, I'm remote in Boston | 1 | 0.00418 |
| Company is in San Antonio, I live 3 hours away in a rural community | 1 | 0.00418 |
| Concord home based | 1 | 0.00418 |
| Concord ma | 1 | 0.00418 |
| Concord, CA | 1 | 0.00418 |
| confidential | 1 | 0.00418 |
| Connecticut | 1 | 0.00418 |
| Conroe | 1 | 0.00418 |
| Conway | 1 | 0.00418 |
| Coon Rapids | 1 | 0.00418 |
| Corinth | 1 | 0.00418 |
| Corning | 1 | 0.00418 |
| Cornwall, Ontario | 1 | 0.00418 |
| corpus christi | 1 | 0.00418 |
| Corrigen | 1 | 0.00418 |
| Cortland | 1 | 0.00418 |
| Cortland, NY | 1 | 0.00418 |
| CORVALLIS | 1 | 0.00418 |
| Cotati | 1 | 0.00418 |
| Cottage Grove | 1 | 0.00418 |
| Country wide | 1 | 0.00418 |
| Coventry UK | 1 | 0.00418 |
| Covina | 1 | 0.00418 |
| Cranston, RI | 1 | 0.00418 |
| Creighton | 1 | 0.00418 |
| Crescent City | 1 | 0.00418 |
| Creston | 1 | 0.00418 |
| Crewe | 1 | 0.00418 |
| Crewe, Cheshire | 1 | 0.00418 |
| Crowley | 1 | 0.00418 |
| Crown Point | 1 | 0.00418 |
| Crownsville | 1 | 0.00418 |
| Crystal City | 1 | 0.00418 |
| Crystal Lake | 1 | 0.00418 |
| Cuernavaca | 1 | 0.00418 |
| Cuffley | 1 | 0.00418 |
| Cullowhee | 1 | 0.00418 |
| Cumbria | 1 | 0.00418 |
| Cupertino, CA | 1 | 0.00418 |
| currently remote | 1 | 0.00418 |
| Cuyahoga Falls | 1 | 0.00418 |
| Cypress | 1 | 0.00418 |
| Dachau | 1 | 0.00418 |
| Daegu | 1 | 0.00418 |
| Dagsboro | 1 | 0.00418 |
| Dahlgren | 1 | 0.00418 |
| Dahlonega | 1 | 0.00418 |
| Dallas / Fort Worth | 1 | 0.00418 |
| Dallas-Ft Worth | 1 | 0.00418 |
| Dallas, Texas | 1 | 0.00418 |
| Dallas, TX | 1 | 0.00418 |
| Dallas/Fort Worth | 1 | 0.00418 |
| Dallas/Fort Worth region | 1 | 0.00418 |
| Daly City | 1 | 0.00418 |
| Danbury CT | 1 | 0.00418 |
| Darmstadt | 1 | 0.00418 |
| Dartmouth | 1 | 0.00418 |
| Davenport | 1 | 0.00418 |
| Davenport area | 1 | 0.00418 |
| Daytona | 1 | 0.00418 |
| DC area | 1 | 0.00418 |
| DC Area | 1 | 0.00418 |
| DC Metro Area | 1 | 0.00418 |
| DC metro area, mostly DC and Montgomery County MD | 1 | 0.00418 |
| DC suburb | 1 | 0.00418 |
| DC Suburb | 1 | 0.00418 |
| Dearborn Heights | 1 | 0.00418 |
| Decherd | 1 | 0.00418 |
| Decline to mention | 1 | 0.00418 |
| Decline to provide | 1 | 0.00418 |
| Decline to State | 1 | 0.00418 |
| Declined | 1 | 0.00418 |
| Declined - Rural area | 1 | 0.00418 |
| Dedham | 1 | 0.00418 |
| Deerfield | 1 | 0.00418 |
| Defiance | 1 | 0.00418 |
| Deforest | 1 | 0.00418 |
| Deland | 1 | 0.00418 |
| DeLand | 1 | 0.00418 |
| Delaware County | 1 | 0.00418 |
| Delmar | 1 | 0.00418 |
| Delray beach | 1 | 0.00418 |
| Den bosch | 1 | 0.00418 |
| denver | 1 | 0.00418 |
| Denver - remote | 1 | 0.00418 |
| Denver - remote, company based in DC | 1 | 0.00418 |
| Denver (but remote - company is NYC) | 1 | 0.00418 |
| Denver Metro | 1 | 0.00418 |
| Denver-metro | 1 | 0.00418 |
| Denver, Chicago | 1 | 0.00418 |
| Derby, CT | 1 | 0.00418 |
| DeRidder | 1 | 0.00418 |
| Derry | 1 | 0.00418 |
| Des moines | 1 | 0.00418 |
| Detroit Area | 1 | 0.00418 |
| Detroit Metro area | 1 | 0.00418 |
| Detroit Metro Area | 1 | 0.00418 |
| devens | 1 | 0.00418 |
| Devens | 1 | 0.00418 |
| Dfw | 1 | 0.00418 |
| DFW area | 1 | 0.00418 |
| Dhaka | 1 | 0.00418 |
| Dickinson | 1 | 0.00418 |
| Didcot | 1 | 0.00418 |
| Dillard (I’m a telecommuter for an office based in Orlando FL) | 1 | 0.00418 |
| District of columbia | 1 | 0.00418 |
| Dobbs ferry | 1 | 0.00418 |
| Dodge City | 1 | 0.00418 |
| Doha | 1 | 0.00418 |
| Don’t want to answer | 1 | 0.00418 |
| Doncaster | 1 | 0.00418 |
| Dont want to say | 1 | 0.00418 |
| Dorset | 1 | 0.00418 |
| Dortmund | 1 | 0.00418 |
| Douglas | 1 | 0.00418 |
| Dresden | 1 | 0.00418 |
| Dripping Springs | 1 | 0.00418 |
| Drop down menur was not working. Indianapolis Indiana. | 1 | 0.00418 |
| Dubai | 1 | 0.00418 |
| Dubbo | 1 | 0.00418 |
| Dubli | 1 | 0.00418 |
| Dublin Ireland | 1 | 0.00418 |
| Dublin, CA | 1 | 0.00418 |
| Dublin, Ireland | 1 | 0.00418 |
| Duesseldorf | 1 | 0.00418 |
| Duncanville | 1 | 0.00418 |
| Dundee | 1 | 0.00418 |
| Dunfermline | 1 | 0.00418 |
| Dunnville | 1 | 0.00418 |
| Durham (remote employee in Vermont; salaries based on HQ in Durham) | 1 | 0.00418 |
| Durham area | 1 | 0.00418 |
| Durham, NC | 1 | 0.00418 |
| Duxford | 1 | 0.00418 |
| Eagan, MN | 1 | 0.00418 |
| Eagle | 1 | 0.00418 |
| East Bay | 1 | 0.00418 |
| East Brunswick | 1 | 0.00418 |
| East Chicago | 1 | 0.00418 |
| East Coast | 1 | 0.00418 |
| East Coast USA | 1 | 0.00418 |
| East Greenbush | 1 | 0.00418 |
| East Hampton | 1 | 0.00418 |
| East hartford | 1 | 0.00418 |
| EAST LANSING | 1 | 0.00418 |
| East Liverpool | 1 | 0.00418 |
| East Providence | 1 | 0.00418 |
| East Rockaway (Long Island) | 1 | 0.00418 |
| East Rutherford | 1 | 0.00418 |
| East Stroudsburg | 1 | 0.00418 |
| East Sussex | 1 | 0.00418 |
| East Syracuse | 1 | 0.00418 |
| Eastern | 1 | 0.00418 |
| Eastern MA | 1 | 0.00418 |
| Eastern MA (not Boston) | 1 | 0.00418 |
| Eastern WA | 1 | 0.00418 |
| Eastern WI City; approx. pop 50,000 | 1 | 0.00418 |
| Easthampton | 1 | 0.00418 |
| Eatontown, NJ | 1 | 0.00418 |
| Eau Claire, WI | 1 | 0.00418 |
| eden prairie | 1 | 0.00418 |
| Eden prairie | 1 | 0.00418 |
| Eden Prairie, MN | 1 | 0.00418 |
| Edgartown ma | 1 | 0.00418 |
| Edgewood | 1 | 0.00418 |
| EDINA | 1 | 0.00418 |
| Edina - Minneapolis | 1 | 0.00418 |
| Edinburg | 1 | 0.00418 |
| Edinburgh (Scotland) | 1 | 0.00418 |
| Edinburgh, UK | 1 | 0.00418 |
| Edinburgh/London/Dublin/Manchester | 1 | 0.00418 |
| Edison, NJ | 1 | 0.00418 |
| Edmond | 1 | 0.00418 |
| EDMONTON | 1 | 0.00418 |
| Edson | 1 | 0.00418 |
| Egg Harbor Township | 1 | 0.00418 |
| Egg harbor twp | 1 | 0.00418 |
| Egham | 1 | 0.00418 |
| Eglin AFB | 1 | 0.00418 |
| Eindhoven | 1 | 0.00418 |
| El Dorado | 1 | 0.00418 |
| El jadida | 1 | 0.00418 |
| El Monte | 1 | 0.00418 |
| El segundo | 1 | 0.00418 |
| Elgin | 1 | 0.00418 |
| Elgin, Illinois | 1 | 0.00418 |
| Elkford | 1 | 0.00418 |
| Elko | 1 | 0.00418 |
| Elkton | 1 | 0.00418 |
| Elkton, VA | 1 | 0.00418 |
| Ellensburg, WA | 1 | 0.00418 |
| Elmhurst | 1 | 0.00418 |
| Elmwood Park | 1 | 0.00418 |
| Emporia | 1 | 0.00418 |
| Enfield | 1 | 0.00418 |
| Enon | 1 | 0.00418 |
| erie | 1 | 0.00418 |
| Erlanger | 1 | 0.00418 |
| Esbjerg | 1 | 0.00418 |
| Escanaba | 1 | 0.00418 |
| Espoo | 1 | 0.00418 |
| Essen | 1 | 0.00418 |
| Essex county | 1 | 0.00418 |
| Essex County | 1 | 0.00418 |
| Estero | 1 | 0.00418 |
| Estevan, Saskatchewan | 1 | 0.00418 |
| Ethel | 1 | 0.00418 |
| Eton, Berkshire | 1 | 0.00418 |
| Euclid | 1 | 0.00418 |
| Eugene, OR | 1 | 0.00418 |
| Everett, WA | 1 | 0.00418 |
| Ewing | 1 | 0.00418 |
| expat in Spanish speaking country | 1 | 0.00418 |
| Fairbury | 1 | 0.00418 |
| fairfax | 1 | 0.00418 |
| Fairfax (but now permanently virtual) | 1 | 0.00418 |
| Fairfax Station | 1 | 0.00418 |
| Fairfax VA | 1 | 0.00418 |
| Fairfax, va | 1 | 0.00418 |
| Fairfield County | 1 | 0.00418 |
| Falcon Heights | 1 | 0.00418 |
| Faribault | 1 | 0.00418 |
| Farmington hills | 1 | 0.00418 |
| federal way | 1 | 0.00418 |
| Fennimore | 1 | 0.00418 |
| Festus | 1 | 0.00418 |
| Few hours outside Columbus | 1 | 0.00418 |
| Field employee | 1 | 0.00418 |
| Field Employee | 1 | 0.00418 |
| Findlay | 1 | 0.00418 |
| Findlay, OH | 1 | 0.00418 |
| Fish Creek | 1 | 0.00418 |
| Fitchburg | 1 | 0.00418 |
| Fitchburg and Worcester | 1 | 0.00418 |
| Flint | 1 | 0.00418 |
| Florida | 1 | 0.00418 |
| Flowood | 1 | 0.00418 |
| Foothill Ranch, CA | 1 | 0.00418 |
| Fords | 1 | 0.00418 |
| Forest | 1 | 0.00418 |
| Forest Hill | 1 | 0.00418 |
| Forest Park | 1 | 0.00418 |
| Fort Belvoir | 1 | 0.00418 |
| Fort bragg | 1 | 0.00418 |
| Fort Campbell | 1 | 0.00418 |
| fort collins | 1 | 0.00418 |
| Fort Eustis | 1 | 0.00418 |
| Fort Fairfield | 1 | 0.00418 |
| Fort Hood | 1 | 0.00418 |
| Fort Knox | 1 | 0.00418 |
| Fort Lee | 1 | 0.00418 |
| Fort Meade | 1 | 0.00418 |
| Fort Pierce | 1 | 0.00418 |
| Fort Plain | 1 | 0.00418 |
| fort Polk | 1 | 0.00418 |
| Fort Rucker | 1 | 0.00418 |
| Fort Walton Beach | 1 | 0.00418 |
| Fort Walton Beach, FL | 1 | 0.00418 |
| Fortville | 1 | 0.00418 |
| foster city | 1 | 0.00418 |
| Fountain | 1 | 0.00418 |
| Fountain Valley | 1 | 0.00418 |
| Fox Valley | 1 | 0.00418 |
| Foxborough | 1 | 0.00418 |
| France | 1 | 0.00418 |
| Frankfurt a.M. | 1 | 0.00418 |
| Frankfurt am Main | 1 | 0.00418 |
| Frankfurt area | 1 | 0.00418 |
| Franklin Lakes | 1 | 0.00418 |
| Franklin Springs | 1 | 0.00418 |
| Franklin TN | 1 | 0.00418 |
| Franklin, MA | 1 | 0.00418 |
| Fraser | 1 | 0.00418 |
| Fraser valley | 1 | 0.00418 |
| Frederick, MD | 1 | 0.00418 |
| Free | 1 | 0.00418 |
| Freeland | 1 | 0.00418 |
| Freetown | 1 | 0.00418 |
| French Camp | 1 | 0.00418 |
| from home | 1 | 0.00418 |
| from home (company based in South of England near London) | 1 | 0.00418 |
| From home for a chicago based company | 1 | 0.00418 |
| Ft lauderdale | 1 | 0.00418 |
| Ft Myers | 1 | 0.00418 |
| Ft wayne | 1 | 0.00418 |
| Ft. Lauderdale | 1 | 0.00418 |
| Ft. Mitchell | 1 | 0.00418 |
| Ft. Walton | 1 | 0.00418 |
| Ft. Worth | 1 | 0.00418 |
| Full time remote employee (prior to pandemic) | 1 | 0.00418 |
| Full time remote employee, pre-pandemic | 1 | 0.00418 |
| Full time remote from my home near a major PA city | 1 | 0.00418 |
| Fully Remote - customers around the country | 1 | 0.00418 |
| Fully Remote (Greater Boston) | 1 | 0.00418 |
| Fully remote company based out of CA | 1 | 0.00418 |
| Fuquay Varina | 1 | 0.00418 |
| Fürth | 1 | 0.00418 |
| G | 1 | 0.00418 |
| Gadsden | 1 | 0.00418 |
| Gaitherburg | 1 | 0.00418 |
| Galax | 1 | 0.00418 |
| Galicia | 1 | 0.00418 |
| Gallatin | 1 | 0.00418 |
| Galloway/Atlantic City | 1 | 0.00418 |
| Garden city | 1 | 0.00418 |
| Garden Grove | 1 | 0.00418 |
| Gardiner | 1 | 0.00418 |
| Garland | 1 | 0.00418 |
| Garner | 1 | 0.00418 |
| Garnet Valley | 1 | 0.00418 |
| Gaston | 1 | 0.00418 |
| Gastonia | 1 | 0.00418 |
| Gateshead | 1 | 0.00418 |
| Gatesville | 1 | 0.00418 |
| Gatineau | 1 | 0.00418 |
| GC | 1 | 0.00418 |
| Geismar | 1 | 0.00418 |
| Geismar, LA | 1 | 0.00418 |
| Geneva | 1 | 0.00418 |
| Georgian Bay area | 1 | 0.00418 |
| Germantown | 1 | 0.00418 |
| Geyserville | 1 | 0.00418 |
| Ghent | 1 | 0.00418 |
| Gig Harbor | 1 | 0.00418 |
| Gilbertsville | 1 | 0.00418 |
| Glassboro | 1 | 0.00418 |
| Glen Burnie | 1 | 0.00418 |
| Glen Ellyn | 1 | 0.00418 |
| glendale | 1 | 0.00418 |
| Glendale, but I'm full time remote. | 1 | 0.00418 |
| Glendora | 1 | 0.00418 |
| Glennville | 1 | 0.00418 |
| Glenside | 1 | 0.00418 |
| Glenview | 1 | 0.00418 |
| Glenwood Springs | 1 | 0.00418 |
| Gloucestershire | 1 | 0.00418 |
| Gloucestershire (v. rural place of work) | 1 | 0.00418 |
| Gloversville, NY | 1 | 0.00418 |
| Goldsboro | 1 | 0.00418 |
| Goodyear | 1 | 0.00418 |
| Gorham/Portland | 1 | 0.00418 |
| Goring by Sea | 1 | 0.00418 |
| Goshen | 1 | 0.00418 |
| Göttingen | 1 | 0.00418 |
| Granbury | 1 | 0.00418 |
| Grand Haven | 1 | 0.00418 |
| Grand Prairie | 1 | 0.00418 |
| GRAND RAPIDS | 1 | 0.00418 |
| Grande Prairie Alberta Canada | 1 | 0.00418 |
| Grande Prairie, AB | 1 | 0.00418 |
| Grandville | 1 | 0.00418 |
| Grants | 1 | 0.00418 |
| Grants, NM | 1 | 0.00418 |
| Grays Harbor County | 1 | 0.00418 |
| Graz | 1 | 0.00418 |
| Great Falls | 1 | 0.00418 |
| Greater Atlanta Area | 1 | 0.00418 |
| Greater boston | 1 | 0.00418 |
| greater boston area | 1 | 0.00418 |
| Greater Chicago | 1 | 0.00418 |
| greater Chicagoland | 1 | 0.00418 |
| Greater Chicagoland | 1 | 0.00418 |
| Greater Madison Area | 1 | 0.00418 |
| Greater Minneapolis metro area | 1 | 0.00418 |
| Greater Nashville Area | 1 | 0.00418 |
| Greater New Orleans Metro Region | 1 | 0.00418 |
| Greater NYC area | 1 | 0.00418 |
| Greater NYC Area | 1 | 0.00418 |
| Greater NYC Metro | 1 | 0.00418 |
| Greater Philadelphia | 1 | 0.00418 |
| Greater Philadelphia Area | 1 | 0.00418 |
| Greater Portland | 1 | 0.00418 |
| Greater Sacramento | 1 | 0.00418 |
| greater Seattle area | 1 | 0.00418 |
| Greater Seattle area | 1 | 0.00418 |
| Greater Vancouver | 1 | 0.00418 |
| Greeley surrounding area | 1 | 0.00418 |
| Green bay | 1 | 0.00418 |
| Green Oaks | 1 | 0.00418 |
| Green River | 1 | 0.00418 |
| Greene | 1 | 0.00418 |
| Greeneville | 1 | 0.00418 |
| Greenfield, MA | 1 | 0.00418 |
| Greensburg | 1 | 0.00418 |
| Greenwood | 1 | 0.00418 |
| Greenwood Village | 1 | 0.00418 |
| Griffin | 1 | 0.00418 |
| Grimes | 1 | 0.00418 |
| Groningen | 1 | 0.00418 |
| Grove City (100% remote) | 1 | 0.00418 |
| Groveland | 1 | 0.00418 |
| Guelph, Ontario | 1 | 0.00418 |
| Guilford | 1 | 0.00418 |
| Gurley | 1 | 0.00418 |
| Gwinnett County | 1 | 0.00418 |
| Gympie | 1 | 0.00418 |
| Haddon field NJ | 1 | 0.00418 |
| Haddonfield | 1 | 0.00418 |
| Hadley | 1 | 0.00418 |
| hagerstown | 1 | 0.00418 |
| Haines city | 1 | 0.00418 |
| Halesowen | 1 | 0.00418 |
| Hamlet | 1 | 0.00418 |
| Hampshire | 1 | 0.00418 |
| hampton | 1 | 0.00418 |
| Hannibal | 1 | 0.00418 |
| Hannover | 1 | 0.00418 |
| Hanoi | 1 | 0.00418 |
| Hanover area | 1 | 0.00418 |
| Harlingen, TX (Remote Work) | 1 | 0.00418 |
| Harper Woods | 1 | 0.00418 |
| Harrisburg area | 1 | 0.00418 |
| Harrisburg Area | 1 | 0.00418 |
| Harrisburg Region | 1 | 0.00418 |
| Harrisburg, PA, USA | 1 | 0.00418 |
| Harrison | 1 | 0.00418 |
| Harrodsburg | 1 | 0.00418 |
| Hartford area | 1 | 0.00418 |
| Hartford County | 1 | 0.00418 |
| Hartforz | 1 | 0.00418 |
| Hartland | 1 | 0.00418 |
| Hartsville | 1 | 0.00418 |
| Hatboro | 1 | 0.00418 |
| Hauppauge | 1 | 0.00418 |
| Haverhill | 1 | 0.00418 |
| HAVERHILL | 1 | 0.00418 |
| Havre | 1 | 0.00418 |
| Hawalli | 1 | 0.00418 |
| hawera | 1 | 0.00418 |
| Hawley | 1 | 0.00418 |
| hazlet | 1 | 0.00418 |
| Healdsburg | 1 | 0.00418 |
| HELENA | 1 | 0.00418 |
| helsinki | 1 | 0.00418 |
| Hemel Hempstead | 1 | 0.00418 |
| Henrico | 1 | 0.00418 |
| Henrico County | 1 | 0.00418 |
| Henrico, VA | 1 | 0.00418 |
| Hercules | 1 | 0.00418 |
| Heredia | 1 | 0.00418 |
| Hereford | 1 | 0.00418 |
| Hermosillo, Sonora | 1 | 0.00418 |
| Herndon VA | 1 | 0.00418 |
| Herndon, Virginia | 1 | 0.00418 |
| Hersey | 1 | 0.00418 |
| Hershey | 1 | 0.00418 |
| Hertfordshire | 1 | 0.00418 |
| Hiawatha | 1 | 0.00418 |
| Hicksville | 1 | 0.00418 |
| Highland | 1 | 0.00418 |
| Highland Heights | 1 | 0.00418 |
| Highlands Ranch | 1 | 0.00418 |
| Hill City | 1 | 0.00418 |
| Hilliard | 1 | 0.00418 |
| Hillsdale | 1 | 0.00418 |
| Hilton Head area | 1 | 0.00418 |
| Hilversum | 1 | 0.00418 |
| Hinckley | 1 | 0.00418 |
| Hines | 1 | 0.00418 |
| Hingham | 1 | 0.00418 |
| Hobart | 1 | 0.00418 |
| HOBART | 1 | 0.00418 |
| Hobart, Tasmania | 1 | 0.00418 |
| Hoffman Estates | 1 | 0.00418 |
| Hoffman Estates (suburb of Chicago) | 1 | 0.00418 |
| Holbrook | 1 | 0.00418 |
| Holmdel | 1 | 0.00418 |
| Holmdel, NJ | 1 | 0.00418 |
| Home (near Montreal) | 1 | 0.00418 |
| Home based | 1 | 0.00418 |
| Home Based | 1 | 0.00418 |
| Home office | 1 | 0.00418 |
| Home plus travel to customers. Near Chicago | 1 | 0.00418 |
| Home worker, North East England | 1 | 0.00418 |
| Home/remote | 1 | 0.00418 |
| Hong Kong | 1 | 0.00418 |
| Honolilu | 1 | 0.00418 |
| Hood River | 1 | 0.00418 |
| Hopewell | 1 | 0.00418 |
| Hopkins | 1 | 0.00418 |
| Hopkinton | 1 | 0.00418 |
| Houghton | 1 | 0.00418 |
| Houston Area | 1 | 0.00418 |
| Houston but I am fully remote permanently | 1 | 0.00418 |
| Houston Texas | 1 | 0.00418 |
| Houston-Galveston-Brazoria area | 1 | 0.00418 |
| Houston, but the job is remote | 1 | 0.00418 |
| Howe | 1 | 0.00418 |
| Howell | 1 | 0.00418 |
| Hoyt Lakes | 1 | 0.00418 |
| HQ us in Cambridge, Ma but moving to the suburbs. I live right outside of Boston. | 1 | 0.00418 |
| Huber Heights | 1 | 0.00418 |
| Hudson Valley NY | 1 | 0.00418 |
| Hudson, NY | 1 | 0.00418 |
| Hughenden | 1 | 0.00418 |
| Hunt valley | 1 | 0.00418 |
| Huntersville | 1 | 0.00418 |
| Huntington (remote, HQ is in Charleston) | 1 | 0.00418 |
| Huntington beach | 1 | 0.00418 |
| Huntington Station | 1 | 0.00418 |
| Hutchinson | 1 | 0.00418 |
| Hüttigweiler | 1 | 0.00418 |
| Hyannis | 1 | 0.00418 |
| Hyattsville | 1 | 0.00418 |
| Hyde Park | 1 | 0.00418 |
| I don’t live in a city; I am VERY rural | 1 | 0.00418 |
| I left Boston for Indiana, so get a Boston salary in south bend | 1 | 0.00418 |
| I live in Orlando, but I am full-time remote | 1 | 0.00418 |
| I travel every week to different cities. | 1 | 0.00418 |
| I WFH full time for a remote organization (pre-pandemic) | 1 | 0.00418 |
| I work from home | 1 | 0.00418 |
| I work from home (permanent) | 1 | 0.00418 |
| I work majority for companies in the state of California. | 1 | 0.00418 |
| I work remotely | 1 | 0.00418 |
| I work remotely but in non-COVID times I work at client sites throughout the US | 1 | 0.00418 |
| I work remotely with clients across the country | 1 | 0.00418 |
| I work remotely, but based in San Diego | 1 | 0.00418 |
| I work remotely. My company is based out of Pittsburgh, PA | 1 | 0.00418 |
| I'm not comfortable answering that. Small town with something around 5k people. | 1 | 0.00418 |
| I'm permanently remote in MA, company is global. | 1 | 0.00418 |
| I'm sorry, I'd rather not say. But Alberta in Canada. | 1 | 0.00418 |
| I’m remote but my company is based in Massachusetts | 1 | 0.00418 |
| Idaho Falls | 1 | 0.00418 |
| Illinois, Iowa, Missouri | 1 | 0.00418 |
| Imperial | 1 | 0.00418 |
| In various cities. I work on projects through my employer for various financial organisations in the field of compliance & risk. My new project will be based in Amsterdam. | 1 | 0.00418 |
| Independence | 1 | 0.00418 |
| independence (cincinnati area) | 1 | 0.00418 |
| Indian Head | 1 | 0.00418 |
| Indian Head Park, IL | 1 | 0.00418 |
| Ingolstadt | 1 | 0.00418 |
| Innsbruck | 1 | 0.00418 |
| Invercargill | 1 | 0.00418 |
| Inverness | 1 | 0.00418 |
| Inwood | 1 | 0.00418 |
| Iowa | 1 | 0.00418 |
| Iowa City (Coralville) | 1 | 0.00418 |
| Iowa City / Cedar Rapids | 1 | 0.00418 |
| ipswich | 1 | 0.00418 |
| Iqaluit | 1 | 0.00418 |
| Ironwood | 1 | 0.00418 |
| Isabella | 1 | 0.00418 |
| Ishpeming | 1 | 0.00418 |
| Islamabad | 1 | 0.00418 |
| Islip | 1 | 0.00418 |
| Ithaca ny | 1 | 0.00418 |
| Ithaca NY | 1 | 0.00418 |
| Ithaca, NY | 1 | 0.00418 |
| Jackson Center | 1 | 0.00418 |
| Jackson Hole | 1 | 0.00418 |
| Jackson, MI | 1 | 0.00418 |
| Jackson, MS | 1 | 0.00418 |
| Jacksonville, FL | 1 | 0.00418 |
| Janesville | 1 | 0.00418 |
| Jasper | 1 | 0.00418 |
| Jax | 1 | 0.00418 |
| Jena | 1 | 0.00418 |
| Jenkintown | 1 | 0.00418 |
| Jersey city | 1 | 0.00418 |
| JERSEY CITY | 1 | 0.00418 |
| Jersey City, NJ | 1 | 0.00418 |
| Jerusalem | 1 | 0.00418 |
| Jessup | 1 | 0.00418 |
| Job is based in DC, but I go to where disasters are | 1 | 0.00418 |
| Johannesburg | 1 | 0.00418 |
| Johnston, RI | 1 | 0.00418 |
| Johnstown | 1 | 0.00418 |
| Joint Base Andrews, MD | 1 | 0.00418 |
| Joppatowne (Near Baltimore, MD) | 1 | 0.00418 |
| Jurupa Valley | 1 | 0.00418 |
| Kabul | 1 | 0.00418 |
| Kajaani | 1 | 0.00418 |
| Kamloops | 1 | 0.00418 |
| Kampala | 1 | 0.00418 |
| Kannapolis | 1 | 0.00418 |
| Kansa City | 1 | 0.00418 |
| Kansas City Region | 1 | 0.00418 |
| Kansas City, MO | 1 | 0.00418 |
| Kapaa | 1 | 0.00418 |
| Karlsruhe | 1 | 0.00418 |
| Kassel | 1 | 0.00418 |
| Keiser | 1 | 0.00418 |
| Kelown | 1 | 0.00418 |
| Kenora | 1 | 0.00418 |
| Kentwood | 1 | 0.00418 |
| Ketchikan | 1 | 0.00418 |
| Kettering | 1 | 0.00418 |
| Key West | 1 | 0.00418 |
| Keyport | 1 | 0.00418 |
| Keyser | 1 | 0.00418 |
| Kiel | 1 | 0.00418 |
| Kigali | 1 | 0.00418 |
| Kilkenny | 1 | 0.00418 |
| Kingsburg | 1 | 0.00418 |
| Kingston Ontario | 1 | 0.00418 |
| Kingston, Ontario | 1 | 0.00418 |
| Kipling | 1 | 0.00418 |
| Kitchener Waterloo | 1 | 0.00418 |
| Kitchener-Waterloo | 1 | 0.00418 |
| Kitchener, ontario | 1 | 0.00418 |
| Kitimat, BC | 1 | 0.00418 |
| Kiyv | 1 | 0.00418 |
| Klamath Falls | 1 | 0.00418 |
| Knightdale | 1 | 0.00418 |
| Köln | 1 | 0.00418 |
| Kragujevac | 1 | 0.00418 |
| Krakow | 1 | 0.00418 |
| Krum | 1 | 0.00418 |
| Kuala Lumpur | 1 | 0.00418 |
| La Laguna | 1 | 0.00418 |
| La Mirada | 1 | 0.00418 |
| La Porte | 1 | 0.00418 |
| Lacey | 1 | 0.00418 |
| Lacey Township | 1 | 0.00418 |
| Lacombe | 1 | 0.00418 |
| Laconia | 1 | 0.00418 |
| Lagos | 1 | 0.00418 |
| Laguna Hills | 1 | 0.00418 |
| Laguna niguel | 1 | 0.00418 |
| LagunaHills | 1 | 0.00418 |
| Lahaina | 1 | 0.00418 |
| Laie | 1 | 0.00418 |
| Lake Buena Vista | 1 | 0.00418 |
| Lake Charles | 1 | 0.00418 |
| Lake City | 1 | 0.00418 |
| Lake Elsinor | 1 | 0.00418 |
| Lake Forest Park | 1 | 0.00418 |
| Lake Mills | 1 | 0.00418 |
| Lake Oswego | 1 | 0.00418 |
| Lake Tahoe | 1 | 0.00418 |
| Lakeville | 1 | 0.00418 |
| Lakewood (live in Denver) | 1 | 0.00418 |
| Lakewood Ranch | 1 | 0.00418 |
| Lakewood, CO | 1 | 0.00418 |
| Lambertville | 1 | 0.00418 |
| Lamham | 1 | 0.00418 |
| Lancashire | 1 | 0.00418 |
| Lancaster PA | 1 | 0.00418 |
| Lancaster UK | 1 | 0.00418 |
| Lancaster, Pa | 1 | 0.00418 |
| Langley | 1 | 0.00418 |
| Langley, BC | 1 | 0.00418 |
| Lanham | 1 | 0.00418 |
| Lanham, MD | 1 | 0.00418 |
| Lansdowne | 1 | 0.00418 |
| Laplace | 1 | 0.00418 |
| Laredo | 1 | 0.00418 |
| Large metro area | 1 | 0.00418 |
| Large town/small city | 1 | 0.00418 |
| Las begas | 1 | 0.00418 |
| Las Palmas | 1 | 0.00418 |
| las vegas | 1 | 0.00418 |
| Las vegas | 1 | 0.00418 |
| Las Vegas (Techinically remote) | 1 | 0.00418 |
| Latham | 1 | 0.00418 |
| Lathma | 1 | 0.00418 |
| Lawrenceburg | 1 | 0.00418 |
| Lawton | 1 | 0.00418 |
| Le Center | 1 | 0.00418 |
| Leadville | 1 | 0.00418 |
| Leavenworth | 1 | 0.00418 |
| Leeds,UK | 1 | 0.00418 |
| Leesburg VA | 1 | 0.00418 |
| LeHigh Valley | 1 | 0.00418 |
| Lehighton | 1 | 0.00418 |
| Leigh, Greater Manchester | 1 | 0.00418 |
| Lemgo | 1 | 0.00418 |
| Lenoir | 1 | 0.00418 |
| Lenox | 1 | 0.00418 |
| Leonia | 1 | 0.00418 |
| lethbridge | 1 | 0.00418 |
| Lethbridge | 1 | 0.00418 |
| Lewisburg | 1 | 0.00418 |
| lewisville | 1 | 0.00418 |
| LEXINGTON | 1 | 0.00418 |
| Lexington Park | 1 | 0.00418 |
| Libby | 1 | 0.00418 |
| Liberal | 1 | 0.00418 |
| Liberty | 1 | 0.00418 |
| Libertyville | 1 | 0.00418 |
| Lillington | 1 | 0.00418 |
| Limerick | 1 | 0.00418 |
| Limestone | 1 | 0.00418 |
| Lincoln, NE | 1 | 0.00418 |
| Lincoln, RI | 1 | 0.00418 |
| Lincolnshire | 1 | 0.00418 |
| Linden | 1 | 0.00418 |
| Lindon | 1 | 0.00418 |
| Linthicum, MD | 1 | 0.00418 |
| Little Chute | 1 | 0.00418 |
| Little rock | 1 | 0.00418 |
| Littlehampton | 1 | 0.00418 |
| Littleton | 1 | 0.00418 |
| Littleton, MA / Manchester, NH | 1 | 0.00418 |
| Ljubljana | 1 | 0.00418 |
| Lloydminster | 1 | 0.00418 |
| Lockport | 1 | 0.00418 |
| Lodi | 1 | 0.00418 |
| Lombard | 1 | 0.00418 |
| Lomdon | 1 | 0.00418 |
| Lompoc | 1 | 0.00418 |
| london | 1 | 0.00418 |
| LONDON | 1 | 0.00418 |
| London (but remote) | 1 | 0.00418 |
| London (outskirts - Croydon) | 1 | 0.00418 |
| London / remote | 1 | 0.00418 |
| London / Remote | 1 | 0.00418 |
| London suburb | 1 | 0.00418 |
| London, ON | 1 | 0.00418 |
| London, ON CAN | 1 | 0.00418 |
| London, Ontario, Canada | 1 | 0.00418 |
| London/home | 1 | 0.00418 |
| Long Beach, Ca | 1 | 0.00418 |
| Long Island, NY | 1 | 0.00418 |
| longbeach | 1 | 0.00418 |
| longford | 1 | 0.00418 |
| Longmont (remote) | 1 | 0.00418 |
| Lons Le Saunier | 1 | 0.00418 |
| Lopen | 1 | 0.00418 |
| Lorton | 1 | 0.00418 |
| Los A6 | 1 | 0.00418 |
| los alamos | 1 | 0.00418 |
| Los alamos | 1 | 0.00418 |
| Los Altos | 1 | 0.00418 |
| Los Altos Hills | 1 | 0.00418 |
| LOS ANGELES | 1 | 0.00418 |
| Los Angeles (but my job is remote) | 1 | 0.00418 |
| Los Angeles area | 1 | 0.00418 |
| Los Angeles Metro | 1 | 0.00418 |
| Los Angeles Metro Area | 1 | 0.00418 |
| Los Angeles, but I work with people in Europe including the company owner | 1 | 0.00418 |
| Los Angeles, or I travel for work | 1 | 0.00418 |
| Los Angles | 1 | 0.00418 |
| Los Gatos | 1 | 0.00418 |
| Loudoun County | 1 | 0.00418 |
| LOUISVILLE | 1 | 0.00418 |
| Louisville (remote) | 1 | 0.00418 |
| lowell | 1 | 0.00418 |
| Lower Hutt, Wellington | 1 | 0.00418 |
| Lusby | 1 | 0.00418 |
| Lutherville | 1 | 0.00418 |
| Luxemburg | 1 | 0.00418 |
| Lyndhurst | 1 | 0.00418 |
| Lyndon | 1 | 0.00418 |
| Lynnfield (Boston metro area) | 1 | 0.00418 |
| lynnwood | 1 | 0.00418 |
| Lyons | 1 | 0.00418 |
| Maassluis | 1 | 0.00418 |
| Maastricht | 1 | 0.00418 |
| Machesney Park | 1 | 0.00418 |
| Macomb | 1 | 0.00418 |
| Macon, GA | 1 | 0.00418 |
| Madera | 1 | 0.00418 |
| madison | 1 | 0.00418 |
| Madison Heights | 1 | 0.00418 |
| Madison WI | 1 | 0.00418 |
| Madison, WI | 1 | 0.00418 |
| Madison, Wisconsin | 1 | 0.00418 |
| Madisonville | 1 | 0.00418 |
| Madrid, Spain | 1 | 0.00418 |
| Magna | 1 | 0.00418 |
| Maidenhead | 1 | 0.00418 |
| Maidstone | 1 | 0.00418 |
| Maine | 1 | 0.00418 |
| Mainz | 1 | 0.00418 |
| Maitland | 1 | 0.00418 |
| Major metropolitan area | 1 | 0.00418 |
| Major state metro area | 1 | 0.00418 |
| Málaga | 1 | 0.00418 |
| Malaysia | 1 | 0.00418 |
| Malvern, PA | 1 | 0.00418 |
| Manasquan, NJ | 1 | 0.00418 |
| Manchester, England | 1 | 0.00418 |
| Manchesyer | 1 | 0.00418 |
| Mandan | 1 | 0.00418 |
| Manhasset | 1 | 0.00418 |
| Manhasset (Long Island) | 1 | 0.00418 |
| Manitowoc | 1 | 0.00418 |
| Mansfield | 1 | 0.00418 |
| Maple grove | 1 | 0.00418 |
| Maple Shade | 1 | 0.00418 |
| Maquoketa | 1 | 0.00418 |
| Maricopa County | 1 | 0.00418 |
| MARIETTA | 1 | 0.00418 |
| Marina | 1 | 0.00418 |
| Marissa | 1 | 0.00418 |
| Marlborough MA | 1 | 0.00418 |
| Marlton, NJ | 1 | 0.00418 |
| Marseille | 1 | 0.00418 |
| Marshall | 1 | 0.00418 |
| Marshalltown | 1 | 0.00418 |
| marshfield | 1 | 0.00418 |
| Martinez | 1 | 0.00418 |
| Martinez (Bay Area) | 1 | 0.00418 |
| Mascoutah | 1 | 0.00418 |
| Mason city | 1 | 0.00418 |
| massachusetts | 1 | 0.00418 |
| Massillon | 1 | 0.00418 |
| Mattawan | 1 | 0.00418 |
| Mauldin | 1 | 0.00418 |
| Maumee | 1 | 0.00418 |
| Mayagüez | 1 | 0.00418 |
| McAllen | 1 | 0.00418 |
| McClean | 1 | 0.00418 |
| Mccomb | 1 | 0.00418 |
| McKinney | 1 | 0.00418 |
| MCKINNEY | 1 | 0.00418 |
| McLean though I travel some | 1 | 0.00418 |
| Mclean, VA outside of DC | 1 | 0.00418 |
| MCOL New England | 1 | 0.00418 |
| Mead | 1 | 0.00418 |
| Meadville | 1 | 0.00418 |
| Mebourne | 1 | 0.00418 |
| Mechelen | 1 | 0.00418 |
| Medford Oregon | 1 | 0.00418 |
| Medical Lake | 1 | 0.00418 |
| Medium size city | 1 | 0.00418 |
| Melbourne Australia | 1 | 0.00418 |
| Melbourne, Victoria | 1 | 0.00418 |
| Mellbourne | 1 | 0.00418 |
| Memphis (but work 100% remote) | 1 | 0.00418 |
| Menasha | 1 | 0.00418 |
| Mendota Heights | 1 | 0.00418 |
| Menomonie | 1 | 0.00418 |
| Mentor, Ohio | 1 | 0.00418 |
| Meriden | 1 | 0.00418 |
| Merrick - my home, but we do have an office in NYC. Main office is in Seattle, WA. | 1 | 0.00418 |
| Merrit Island | 1 | 0.00418 |
| Merseyside | 1 | 0.00418 |
| Mesa, Arizona | 1 | 0.00418 |
| Methuen | 1 | 0.00418 |
| metro | 1 | 0.00418 |
| Metro Boston | 1 | 0.00418 |
| Metro Detroit area | 1 | 0.00418 |
| Metro NYC | 1 | 0.00418 |
| Metro west | 1 | 0.00418 |
| Metro-Detroit | 1 | 0.00418 |
| Metro-west | 1 | 0.00418 |
| Metrowest area | 1 | 0.00418 |
| Metrowest Boston | 1 | 0.00418 |
| Metrowest Boston area | 1 | 0.00418 |
| Mettawa | 1 | 0.00418 |
| Miami area | 1 | 0.00418 |
| Miami FTL metro area | 1 | 0.00418 |
| Michigan | 1 | 0.00418 |
| Mid-Michigan | 1 | 0.00418 |
| Mid-size city | 1 | 0.00418 |
| middleburg | 1 | 0.00418 |
| Middleburg | 1 | 0.00418 |
| Middleburg Heights | 1 | 0.00418 |
| Middlesbrough | 1 | 0.00418 |
| Midlands | 1 | 0.00418 |
| Midlothian | 1 | 0.00418 |
| Midsized city | 1 | 0.00418 |
| Midwest | 1 | 0.00418 |
| Midwest IL | 1 | 0.00418 |
| Midwest region | 1 | 0.00418 |
| Mill Valley | 1 | 0.00418 |
| Milledgeville | 1 | 0.00418 |
| Millville | 1 | 0.00418 |
| Milton Keynes, UK | 1 | 0.00418 |
| Milwaukee metro | 1 | 0.00418 |
| Milwaukee metro area | 1 | 0.00418 |
| Minneapilis | 1 | 0.00418 |
| MINNEAPOLIS | 1 | 0.00418 |
| Minneapolis metro | 1 | 0.00418 |
| Minneapolis, MN | 1 | 0.00418 |
| Minneapolis/St Paul metro area | 1 | 0.00418 |
| Minneapollis | 1 | 0.00418 |
| Minnepolis | 1 | 0.00418 |
| Minnesota | 1 | 0.00418 |
| Minnespolis | 1 | 0.00418 |
| Minocqua | 1 | 0.00418 |
| Minot | 1 | 0.00418 |
| Miseneheimer | 1 | 0.00418 |
| Mission Viejo | 1 | 0.00418 |
| MISSOULA | 1 | 0.00418 |
| missouri city | 1 | 0.00418 |
| Mitchell | 1 | 0.00418 |
| Mohegan Lake | 1 | 0.00418 |
| Moncks Corner | 1 | 0.00418 |
| Monmouth | 1 | 0.00418 |
| Mono, Ontario | 1 | 0.00418 |
| Monongahela | 1 | 0.00418 |
| Monrovia | 1 | 0.00418 |
| Montana | 1 | 0.00418 |
| Montclair | 1 | 0.00418 |
| Montclair (not actual city, but same region for anonymity) | 1 | 0.00418 |
| Montevideo | 1 | 0.00418 |
| Montgomery, AL | 1 | 0.00418 |
| Montgomeryville | 1 | 0.00418 |
| Monticello | 1 | 0.00418 |
| Montpellier | 1 | 0.00418 |
| montreal | 1 | 0.00418 |
| Montrose | 1 | 0.00418 |
| Moon Twp. PA | 1 | 0.00418 |
| Mooresville | 1 | 0.00418 |
| Moose Jaw | 1 | 0.00418 |
| Moose Jaw, sk. | 1 | 0.00418 |
| Morenci | 1 | 0.00418 |
| Morris Plains | 1 | 0.00418 |
| Morristown (REMOTE) | 1 | 0.00418 |
| Moshiem | 1 | 0.00418 |
| Mount Gambier | 1 | 0.00418 |
| Mount laurel | 1 | 0.00418 |
| Mount Pleasant | 1 | 0.00418 |
| Mount Prospect | 1 | 0.00418 |
| Mount Washington | 1 | 0.00418 |
| Mountain view | 1 | 0.00418 |
| Mountainside | 1 | 0.00418 |
| Mountainview | 1 | 0.00418 |
| Mountlake Terrace | 1 | 0.00418 |
| mt arlington | 1 | 0.00418 |
| Mt Pleasant | 1 | 0.00418 |
| Mt Prospect | 1 | 0.00418 |
| Mt. Washington | 1 | 0.00418 |
| Multiple | 1 | 0.00418 |
| Mumbai | 1 | 0.00418 |
| Münster | 1 | 0.00418 |
| Murrieta | 1 | 0.00418 |
| Muscatine | 1 | 0.00418 |
| Muskego | 1 | 0.00418 |
| Muskegon | 1 | 0.00418 |
| My city + industry would ID my employer | 1 | 0.00418 |
| My house | 1 | 0.00418 |
| My job is remote for everyone at my company, but I live in Brooklyn | 1 | 0.00418 |
| Mystic | 1 | 0.00418 |
| N.A. | 1 | 0.00418 |
| N/A - County | 1 | 0.00418 |
| N/A - remote work | 1 | 0.00418 |
| n/a - work from home | 1 | 0.00418 |
| N/A cloud-based | 1 | 0.00418 |
| N/A-Remote | 1 | 0.00418 |
| N\A | 1 | 0.00418 |
| NA (remote) | 1 | 0.00418 |
| NA (remote). Live near Boston, work is based in upstate NY | 1 | 0.00418 |
| NA (work from home in small town, decline to specify) | 1 | 0.00418 |
| Naas | 1 | 0.00418 |
| Nampa | 1 | 0.00418 |
| Nantes | 1 | 0.00418 |
| Nanticoke | 1 | 0.00418 |
| Naperville (Chicago area) | 1 | 0.00418 |
| Naperville IL | 1 | 0.00418 |
| NAPLES | 1 | 0.00418 |
| Naples, Fl | 1 | 0.00418 |
| Narnia | 1 | 0.00418 |
| Narragansett | 1 | 0.00418 |
| Nashville (at home, remote) | 1 | 0.00418 |
| Nashville metro area | 1 | 0.00418 |
| Nashville Metro Area | 1 | 0.00418 |
| National-Remote | 1 | 0.00418 |
| Naval Base | 1 | 0.00418 |
| NE Ohio | 1 | 0.00418 |
| Near Albany | 1 | 0.00418 |
| near Ann Arbor | 1 | 0.00418 |
| Near Boston | 1 | 0.00418 |
| near Buffalo, NY | 1 | 0.00418 |
| Near Chicago | 1 | 0.00418 |
| Near Indianapolis | 1 | 0.00418 |
| Near Manchester, UK | 1 | 0.00418 |
| Near Sacramento (remote work) | 1 | 0.00418 |
| Near Salt Lake City | 1 | 0.00418 |
| near San Antonio | 1 | 0.00418 |
| Near Seattle | 1 | 0.00418 |
| Nelson | 1 | 0.00418 |
| Neopit | 1 | 0.00418 |
| Neptune Beach | 1 | 0.00418 |
| New albany | 1 | 0.00418 |
| New Berlin | 1 | 0.00418 |
| New Bern | 1 | 0.00418 |
| New Braunfels | 1 | 0.00418 |
| New Brighton | 1 | 0.00418 |
| New Bruswick | 1 | 0.00418 |
| New City, NY | 1 | 0.00418 |
| New Glarus | 1 | 0.00418 |
| New Haven metropolitan area | 1 | 0.00418 |
| New Haven/Remote | 1 | 0.00418 |
| New Hyde park | 1 | 0.00418 |
| New Kensington | 1 | 0.00418 |
| New Lenox | 1 | 0.00418 |
| new Orleans | 1 | 0.00418 |
| New orleans | 1 | 0.00418 |
| New Tecumseth | 1 | 0.00418 |
| New Westminster | 1 | 0.00418 |
| New Yor | 1 | 0.00418 |
| New YOrk | 1 | 0.00418 |
| New York Buty | 1 | 0.00418 |
| new York City | 1 | 0.00418 |
| New York CIty | 1 | 0.00418 |
| New York City (The Bronx) | 1 | 0.00418 |
| New York City Suburbs | 1 | 0.00418 |
| New York City, currently remote | 1 | 0.00418 |
| New york City/Manhattan | 1 | 0.00418 |
| New York, New York | 1 | 0.00418 |
| New YorknCity | 1 | 0.00418 |
| Newark (remote now) | 1 | 0.00418 |
| Newark, NJ | 1 | 0.00418 |
| Newberg | 1 | 0.00418 |
| newcastle | 1 | 0.00418 |
| Newcastle, NSW, Australia | 1 | 0.00418 |
| Newcastle, UK | 1 | 0.00418 |
| Newport beach | 1 | 0.00418 |
| Newport Beach, CA | 1 | 0.00418 |
| Newport News, VA | 1 | 0.00418 |
| Newton Falls | 1 | 0.00418 |
| Newton Poppleford | 1 | 0.00418 |
| Niagara Falls | 1 | 0.00418 |
| Niagara Region | 1 | 0.00418 |
| Nijmegen | 1 | 0.00418 |
| no | 1 | 0.00418 |
| no answer | 1 | 0.00418 |
| No city | 1 | 0.00418 |
| Noble | 1 | 0.00418 |
| Noblesville | 1 | 0.00418 |
| none | 1 | 0.00418 |
| None, work from home | 1 | 0.00418 |
| Noordwijk | 1 | 0.00418 |
| Nope | 1 | 0.00418 |
| Norco | 1 | 0.00418 |
| Normal I’ll | 1 | 0.00418 |
| Norman,OK | 1 | 0.00418 |
| Norristowb | 1 | 0.00418 |
| Norristown, PA | 1 | 0.00418 |
| North | 1 | 0.00418 |
| North Adams | 1 | 0.00418 |
| north bay | 1 | 0.00418 |
| North Bergen, NJ | 1 | 0.00418 |
| North Brunswick | 1 | 0.00418 |
| North Cape, PE | 1 | 0.00418 |
| North Charleston | 1 | 0.00418 |
| North Clarendon | 1 | 0.00418 |
| North Coast | 1 | 0.00418 |
| North Fort Myers | 1 | 0.00418 |
| North Georgia | 1 | 0.00418 |
| North Hempstead | 1 | 0.00418 |
| North Jersey | 1 | 0.00418 |
| North Kingstown | 1 | 0.00418 |
| North Mankato | 1 | 0.00418 |
| North Miami | 1 | 0.00418 |
| North of Portland | 1 | 0.00418 |
| North of Toronto | 1 | 0.00418 |
| North Plainfield | 1 | 0.00418 |
| North Platte | 1 | 0.00418 |
| North Reading | 1 | 0.00418 |
| North Sydney | 1 | 0.00418 |
| North Wales | 1 | 0.00418 |
| North West | 1 | 0.00418 |
| Northampton Ma | 1 | 0.00418 |
| Northborough | 1 | 0.00418 |
| Northbrook/ Charlotte | 1 | 0.00418 |
| Northcentral WI | 1 | 0.00418 |
| Northeast Florida | 1 | 0.00418 |
| Northeast Ohio suburb | 1 | 0.00418 |
| Northern B.C. | 1 | 0.00418 |
| Northern California | 1 | 0.00418 |
| northern CO | 1 | 0.00418 |
| Northern Front Range | 1 | 0.00418 |
| Northern Michigan | 1 | 0.00418 |
| northern new york | 1 | 0.00418 |
| northern vermont | 1 | 0.00418 |
| Northern Virginia | 1 | 0.00418 |
| Northern Virginia (Washington, DC metro) | 1 | 0.00418 |
| Northlake | 1 | 0.00418 |
| Northumberland | 1 | 0.00418 |
| Northville | 1 | 0.00418 |
| Northwest | 1 | 0.00418 |
| Northwest Chicago suburbs | 1 | 0.00418 |
| Northwest Georgia | 1 | 0.00418 |
| Northwest Lower MI | 1 | 0.00418 |
| Norton Shores | 1 | 0.00418 |
| Norwood MA | 1 | 0.00418 |
| Not a city | 1 | 0.00418 |
| Not answering | 1 | 0.00418 |
| Not applicable | 1 | 0.00418 |
| Not Applicable (Remote work) | 1 | 0.00418 |
| Not Denver | 1 | 0.00418 |
| Not Detroit | 1 | 0.00418 |
| Not disclosed | 1 | 0.00418 |
| Not Disclosing | 1 | 0.00418 |
| Not identifying, but the Central Valley, CA | 1 | 0.00418 |
| Not provided | 1 | 0.00418 |
| Not saying | 1 | 0.00418 |
| Nottingham, UK | 1 | 0.00418 |
| Nottoway County | 1 | 0.00418 |
| Novato, CA | 1 | 0.00418 |
| nr. London | 1 | 0.00418 |
| NRW | 1 | 0.00418 |
| Nueremberg | 1 | 0.00418 |
| NULL | 1 | 0.00418 |
| NY Metro | 1 | 0.00418 |
| NY metro area but not NYC | 1 | 0.00418 |
| NY Suburb | 1 | 0.00418 |
| Nyack | 1 | 0.00418 |
| nyc | 1 | 0.00418 |
| NYC - Queens | 1 | 0.00418 |
| NYC (but I work remote) | 1 | 0.00418 |
| NYC (remotely) | 1 | 0.00418 |
| NYC metro area | 1 | 0.00418 |
| NYC, currently WFH | 1 | 0.00418 |
| NYC, NY | 1 | 0.00418 |
| Oak Lawn | 1 | 0.00418 |
| Oak Park | 1 | 0.00418 |
| Oakbrook | 1 | 0.00418 |
| Oakbrook Terrace | 1 | 0.00418 |
| oakland | 1 | 0.00418 |
| Oakland and Hayward | 1 | 0.00418 |
| Oakland CA | 1 | 0.00418 |
| Oakland, CA | 1 | 0.00418 |
| Oaxaca de Juarez (home). Company I work for is based in Los Ángeles | 1 | 0.00418 |
| Ocala | 1 | 0.00418 |
| Ocean | 1 | 0.00418 |
| Ocean County | 1 | 0.00418 |
| Office was in Chicago, now WFH in WI | 1 | 0.00418 |
| Office: Lansing, MI and wfh Ypsilani MI | 1 | 0.00418 |
| Ogden - but I work remote. Company is based in MD | 1 | 0.00418 |
| Okanogan | 1 | 0.00418 |
| OKC | 1 | 0.00418 |
| Okemos | 1 | 0.00418 |
| Olean | 1 | 0.00418 |
| Olympia, WA | 1 | 0.00418 |
| Olympic Peninsula | 1 | 0.00418 |
| Omaha Metro | 1 | 0.00418 |
| Onaha | 1 | 0.00418 |
| One of the largest in the state | 1 | 0.00418 |
| Onley | 1 | 0.00418 |
| Orang | 1 | 0.00418 |
| Orange county | 1 | 0.00418 |
| Orange park | 1 | 0.00418 |
| Orange Park | 1 | 0.00418 |
| Orange, MA | 1 | 0.00418 |
| Orangevale | 1 | 0.00418 |
| Oregon | 1 | 0.00418 |
| Oregon City | 1 | 0.00418 |
| Orillia | 1 | 0.00418 |
| Orkney | 1 | 0.00418 |
| Orlando metro area | 1 | 0.00418 |
| Orlando, I work in United States at a Consulate for Mexican Government | 1 | 0.00418 |
| Orléans | 1 | 0.00418 |
| Ormond Beach | 1 | 0.00418 |
| Orono | 1 | 0.00418 |
| Osceola | 1 | 0.00418 |
| Oshawa | 1 | 0.00418 |
| Oswego | 1 | 0.00418 |
| Ottawa area | 1 | 0.00418 |
| Ottawa ON Canada | 1 | 0.00418 |
| Ottawa-Gatineau | 1 | 0.00418 |
| Ottawa, ON | 1 | 0.00418 |
| Ottawa/Gatineau | 1 | 0.00418 |
| Ottoville, OH | 1 | 0.00418 |
| Outer Island | 1 | 0.00418 |
| Outer London | 1 | 0.00418 |
| Outside Boston | 1 | 0.00418 |
| Outside London | 1 | 0.00418 |
| outside of Boston | 1 | 0.00418 |
| outside philadelphia | 1 | 0.00418 |
| OUtside Philadelphia | 1 | 0.00418 |
| Outside the Twin Cities | 1 | 0.00418 |
| Owen Sound | 1 | 0.00418 |
| Oxford, UK | 1 | 0.00418 |
| Oxnard | 1 | 0.00418 |
| Pacifica | 1 | 0.00418 |
| Paducah | 1 | 0.00418 |
| Palermo | 1 | 0.00418 |
| Palm Beach | 1 | 0.00418 |
| Palm Beach, Fl | 1 | 0.00418 |
| Palm Coast | 1 | 0.00418 |
| Palmyra | 1 | 0.00418 |
| Palo Alto, CA | 1 | 0.00418 |
| Panama City Beach | 1 | 0.00418 |
| Panhandle of FL | 1 | 0.00418 |
| Panhandle region | 1 | 0.00418 |
| Paragould | 1 | 0.00418 |
| Parid | 1 | 0.00418 |
| Park City | 1 | 0.00418 |
| Park City (remote/WFH) | 1 | 0.00418 |
| Parker | 1 | 0.00418 |
| Parkersburg | 1 | 0.00418 |
| Parma | 1 | 0.00418 |
| Parry Sound | 1 | 0.00418 |
| Pascagoula, MS | 1 | 0.00418 |
| Pasco | 1 | 0.00418 |
| Passaic | 1 | 0.00418 |
| Passaic County | 1 | 0.00418 |
| Passau | 1 | 0.00418 |
| Patchogue | 1 | 0.00418 |
| Paulding | 1 | 0.00418 |
| Pawtucket | 1 | 0.00418 |
| Payroll is out of Cincinnati | 1 | 0.00418 |
| Peabody | 1 | 0.00418 |
| Peabody, MA | 1 | 0.00418 |
| Peace River | 1 | 0.00418 |
| Pearisburg | 1 | 0.00418 |
| Pearl City | 1 | 0.00418 |
| Pendleton | 1 | 0.00418 |
| pensacola | 1 | 0.00418 |
| Penticton | 1 | 0.00418 |
| Peoria, IL | 1 | 0.00418 |
| Permanent Remote to Toronto | 1 | 0.00418 |
| Perth, Western Australia | 1 | 0.00418 |
| Peterlee | 1 | 0.00418 |
| Petoskey | 1 | 0.00418 |
| Phialdelphia | 1 | 0.00418 |
| Philadelphia (suburbs) | 1 | 0.00418 |
| Philadelphia and Pittsburgh | 1 | 0.00418 |
| Philadelphia area | 1 | 0.00418 |
| Philadelphia Area | 1 | 0.00418 |
| Philadelphia Suburbs | 1 | 0.00418 |
| Philadelphia, PA | 1 | 0.00418 |
| Philadelphia, PA Suburbs | 1 | 0.00418 |
| Philadelphiai | 1 | 0.00418 |
| Philadephia | 1 | 0.00418 |
| Philadlephia | 1 | 0.00418 |
| Philiadelpia | 1 | 0.00418 |
| philly | 1 | 0.00418 |
| Philly | 1 | 0.00418 |
| Phnom Penh | 1 | 0.00418 |
| Phoenix Area | 1 | 0.00418 |
| Phoenix, AZ | 1 | 0.00418 |
| Phoenix/Tempe | 1 | 0.00418 |
| Phoenixville | 1 | 0.00418 |
| Pinehurst | 1 | 0.00418 |
| Pinole | 1 | 0.00418 |
| Pipersville | 1 | 0.00418 |
| Piscataway, NJ | 1 | 0.00418 |
| Pittaburgh | 1 | 0.00418 |
| PITTSBURGH | 1 | 0.00418 |
| Pittsburgh area | 1 | 0.00418 |
| Pittsfiel | 1 | 0.00418 |
| Plainfield | 1 | 0.00418 |
| Plainville | 1 | 0.00418 |
| Plant City | 1 | 0.00418 |
| Platte City | 1 | 0.00418 |
| Plattsburgh, NY | 1 | 0.00418 |
| Plattsville | 1 | 0.00418 |
| Pleasant grove | 1 | 0.00418 |
| Pleasant Grove | 1 | 0.00418 |
| Pleasant Hill | 1 | 0.00418 |
| Pleasant Prarie | 1 | 0.00418 |
| Plymouth (UK) | 1 | 0.00418 |
| Plymouth England | 1 | 0.00418 |
| Pollock Pines | 1 | 0.00418 |
| Pomfret | 1 | 0.00418 |
| Pompano Beach | 1 | 0.00418 |
| PONTYPOOL | 1 | 0.00418 |
| Poole | 1 | 0.00418 |
| Port Austin | 1 | 0.00418 |
| Port Carling, Muskoka, Ontario | 1 | 0.00418 |
| Port Chester | 1 | 0.00418 |
| Port Coquitlam | 1 | 0.00418 |
| Port washington | 1 | 0.00418 |
| Port Washington | 1 | 0.00418 |
| Portage la Prairie, Manitoba | 1 | 0.00418 |
| Porter County | 1 | 0.00418 |
| PORTLAND | 1 | 0.00418 |
| Portland (I work remote for a company out of state) | 1 | 0.00418 |
| Portland metro | 1 | 0.00418 |
| Portland metro area | 1 | 0.00418 |
| Portland Metro area | 1 | 0.00418 |
| Portland OR | 1 | 0.00418 |
| Portland, ME area | 1 | 0.00418 |
| Portland, or | 1 | 0.00418 |
| Portland, OR (remote for a company in CA) | 1 | 0.00418 |
| Portlanf | 1 | 0.00418 |
| Portree | 1 | 0.00418 |
| Posen | 1 | 0.00418 |
| Poth | 1 | 0.00418 |
| Potland | 1 | 0.00418 |
| Poulsbo | 1 | 0.00418 |
| Powhatan | 1 | 0.00418 |
| Prefer not to answer, moderate size canadian city | 1 | 0.00418 |
| prefer not to identify | 1 | 0.00418 |
| Prefer not to provide | 1 | 0.00418 |
| Prefer not to say (Montana is small!) | 1 | 0.00418 |
| prefer not to say, identifiable information when combined with my job title | 1 | 0.00418 |
| Preston, Lancashire | 1 | 0.00418 |
| Princeton area | 1 | 0.00418 |
| Prineville | 1 | 0.00418 |
| Prospect Park | 1 | 0.00418 |
| providence | 1 | 0.00418 |
| Puget Sound Region | 1 | 0.00418 |
| Pulaski | 1 | 0.00418 |
| Puyallup, WA | 1 | 0.00418 |
| QAC | 1 | 0.00418 |
| Quebec | 1 | 0.00418 |
| Québec | 1 | 0.00418 |
| Quebec city | 1 | 0.00418 |
| Queen Creek | 1 | 0.00418 |
| Queens Village | 1 | 0.00418 |
| Queens/Nassau | 1 | 0.00418 |
| Queensbury | 1 | 0.00418 |
| Quito | 1 | 0.00418 |
| Ra'anana | 1 | 0.00418 |
| RACINE | 1 | 0.00418 |
| Radnor | 1 | 0.00418 |
| Raeford | 1 | 0.00418 |
| raleigh | 1 | 0.00418 |
| Raleigh (remote out of VA) | 1 | 0.00418 |
| Raleigh Durham | 1 | 0.00418 |
| Raleigh NC | 1 | 0.00418 |
| Raleigh; role based out of Boston MA | 1 | 0.00418 |
| Ramsbottom | 1 | 0.00418 |
| Rancho Cucamonga | 1 | 0.00418 |
| Rancho Santa Margarita | 1 | 0.00418 |
| rapid city | 1 | 0.00418 |
| Raritan | 1 | 0.00418 |
| Rather not answer | 1 | 0.00418 |
| rather not say | 1 | 0.00418 |
| Rather not say | 1 | 0.00418 |
| rather not say (too identifiable) | 1 | 0.00418 |
| Raynham | 1 | 0.00418 |
| Red bank | 1 | 0.00418 |
| Red Deer | 1 | 0.00418 |
| Red Hook | 1 | 0.00418 |
| Redacted | 1 | 0.00418 |
| redacted (sorry) | 1 | 0.00418 |
| Redding CA | 1 | 0.00418 |
| redmond | 1 | 0.00418 |
| Redwood City, CA | 1 | 0.00418 |
| Refuse to Answer | 1 | 0.00418 |
| Regensburg | 1 | 0.00418 |
| Regina (SK) | 1 | 0.00418 |
| Regina, SK | 1 | 0.00418 |
| regional | 1 | 0.00418 |
| regional city | 1 | 0.00418 |
| Reidsville | 1 | 0.00418 |
| Remote - | 1 | 0.00418 |
| Remote - HQ is in DC | 1 | 0.00418 |
| Remote - Lansdale | 1 | 0.00418 |
| Remote - previously NYC | 1 | 0.00418 |
| Remote - Washington DC Area | 1 | 0.00418 |
| Remote (Chicago) | 1 | 0.00418 |
| Remote (live in Nottingham) | 1 | 0.00418 |
| Remote (Olympia) | 1 | 0.00418 |
| Remote (org based out of DC metro area) | 1 | 0.00418 |
| Remote (Southern CA) | 1 | 0.00418 |
| Remote (this shouldn't be required) | 1 | 0.00418 |
| Remote Based | 1 | 0.00418 |
| Remote but NYC | 1 | 0.00418 |
| Remote job | 1 | 0.00418 |
| Remote many cities | 1 | 0.00418 |
| Remote near Worcester | 1 | 0.00418 |
| Remote Office | 1 | 0.00418 |
| Remote position | 1 | 0.00418 |
| Remote US | 1 | 0.00418 |
| Remote WFH currently | 1 | 0.00418 |
| Remote work | 1 | 0.00418 |
| Remote work from home | 1 | 0.00418 |
| Remote work in CA, company is in WA | 1 | 0.00418 |
| Remote work; Ontario-wide | 1 | 0.00418 |
| Remote Worker | 1 | 0.00418 |
| Remote Worker out of Wilmington | 1 | 0.00418 |
| Remote worker, staffed out of Cincinnati | 1 | 0.00418 |
| Remote--don't work in same state as my employer is located | 1 | 0.00418 |
| Remote, based out of Baltimore, MD metro area | 1 | 0.00418 |
| Remote, company is based in Raleigh NC | 1 | 0.00418 |
| Remote, from a small city in Romania | 1 | 0.00418 |
| Remote, North Carolina | 1 | 0.00418 |
| Remote; based out of Hudson | 1 | 0.00418 |
| Remote. Company is in Boston, I am in SF | 1 | 0.00418 |
| Remote/ home | 1 | 0.00418 |
| Remote/London | 1 | 0.00418 |
| Remote/NYC HQ | 1 | 0.00418 |
| Remotely - Stamford HQ | 1 | 0.00418 |
| Remotely in NC for DC employer | 1 | 0.00418 |
| remove | 1 | 0.00418 |
| reno | 1 | 0.00418 |
| Reno/Sparks | 1 | 0.00418 |
| renton | 1 | 0.00418 |
| Research Triangle region | 1 | 0.00418 |
| Reston, VA | 1 | 0.00418 |
| Rexburg | 1 | 0.00418 |
| Reynoldsburg | 1 | 0.00418 |
| Richfield | 1 | 0.00418 |
| Richland, WA | 1 | 0.00418 |
| RICHMOND | 1 | 0.00418 |
| RICHMOND VA | 1 | 0.00418 |
| Richmond VIC | 1 | 0.00418 |
| Ridgecrest | 1 | 0.00418 |
| Ridgefield Park | 1 | 0.00418 |
| Ringwood | 1 | 0.00418 |
| Rio de Janeiro | 1 | 0.00418 |
| Rio Rancho | 1 | 0.00418 |
| River Grove | 1 | 0.00418 |
| Riverside and San Bernardino | 1 | 0.00418 |
| Riverside, CA | 1 | 0.00418 |
| ROCHESTER | 1 | 0.00418 |
| Rochester (remote-company based in SF, CA) | 1 | 0.00418 |
| Rock Island | 1 | 0.00418 |
| Rock Springs | 1 | 0.00418 |
| Rocklin (Sacramento area) | 1 | 0.00418 |
| Rockport | 1 | 0.00418 |
| Rocky Hill | 1 | 0.00418 |
| Rohnert Park, CA | 1 | 0.00418 |
| Romeoville | 1 | 0.00418 |
| Rootstown | 1 | 0.00418 |
| Roseland | 1 | 0.00418 |
| Roselle | 1 | 0.00418 |
| Round Rock | 1 | 0.00418 |
| Roxboro | 1 | 0.00418 |
| Royersford | 1 | 0.00418 |
| ROYERSFORD | 1 | 0.00418 |
| RTP | 1 | 0.00418 |
| RTP (Raleigh-Durham) | 1 | 0.00418 |
| Rugby | 1 | 0.00418 |
| Rupert | 1 | 0.00418 |
| Rural - northern | 1 | 0.00418 |
| Rural Alberta | 1 | 0.00418 |
| rural area | 1 | 0.00418 |
| Rural Area | 1 | 0.00418 |
| Rural California | 1 | 0.00418 |
| rural college community | 1 | 0.00418 |
| rural college town | 1 | 0.00418 |
| rural Florida | 1 | 0.00418 |
| Rural Florida Panhandle | 1 | 0.00418 |
| Rural Iowa | 1 | 0.00418 |
| Rural Ireland | 1 | 0.00418 |
| Rural Missouri | 1 | 0.00418 |
| Rural New England | 1 | 0.00418 |
| Rural NW IL | 1 | 0.00418 |
| Rural Richmond Area | 1 | 0.00418 |
| Rural school division | 1 | 0.00418 |
| rural small town | 1 | 0.00418 |
| Rural small town western NY | 1 | 0.00418 |
| Rural Southwest England | 1 | 0.00418 |
| Rural town (prefer not to disclose) | 1 | 0.00418 |
| Rural Upper Peninsula | 1 | 0.00418 |
| Rural west-central MN | 1 | 0.00418 |
| Rural/Suburban | 1 | 0.00418 |
| Ruther Glen | 1 | 0.00418 |
| Rutland | 1 | 0.00418 |
| Rye Brook | 1 | 0.00418 |
| Saarbrücken | 1 | 0.00418 |
| Sabetha | 1 | 0.00418 |
| sacramento | 1 | 0.00418 |
| Sacramento CA | 1 | 0.00418 |
| Saddle Brook | 1 | 0.00418 |
| Saint Charles | 1 | 0.00418 |
| Saint Helena | 1 | 0.00418 |
| Saint Paul Park | 1 | 0.00418 |
| Saint Paul, MN | 1 | 0.00418 |
| Salamanca | 1 | 0.00418 |
| Salem (remote for Boston Mass) | 1 | 0.00418 |
| Salem, MA | 1 | 0.00418 |
| Salisbury | 1 | 0.00418 |
| Salmon | 1 | 0.00418 |
| Salmon Arm | 1 | 0.00418 |
| Salt lake | 1 | 0.00418 |
| Sam Francisco | 1 | 0.00418 |
| Sam Mateo | 1 | 0.00418 |
| San Angelo, Texas | 1 | 0.00418 |
| San antoni | 1 | 0.00418 |
| San Bruno | 1 | 0.00418 |
| San Carlos | 1 | 0.00418 |
| San Clemente | 1 | 0.00418 |
| San Deigo | 1 | 0.00418 |
| San Dieg | 1 | 0.00418 |
| san diego | 1 | 0.00418 |
| San Diego, CA | 1 | 0.00418 |
| San Francisco (nominally; I work from home now due to COVID) | 1 | 0.00418 |
| San Francisco, but company is Boston based | 1 | 0.00418 |
| San Francisco, CA | 1 | 0.00418 |
| San Fransico | 1 | 0.00418 |
| San Fransisco | 1 | 0.00418 |
| san jose | 1 | 0.00418 |
| San jose | 1 | 0.00418 |
| San Jose, CA and Tulsa, OK | 1 | 0.00418 |
| San juan | 1 | 0.00418 |
| San luis obispo | 1 | 0.00418 |
| San Marino | 1 | 0.00418 |
| San Mateo, CA | 1 | 0.00418 |
| san rafael | 1 | 0.00418 |
| Sandy Springs | 1 | 0.00418 |
| Santa Ana, CA | 1 | 0.00418 |
| Santa barbara | 1 | 0.00418 |
| Santa Barbara, CA | 1 | 0.00418 |
| Santa Clarita | 1 | 0.00418 |
| Santee | 1 | 0.00418 |
| Santiago | 1 | 0.00418 |
| Saranac Lake (it's a small town!) | 1 | 0.00418 |
| Sarnia | 1 | 0.00418 |
| saskatoon | 1 | 0.00418 |
| Sauk Centre | 1 | 0.00418 |
| Sauk City | 1 | 0.00418 |
| Sault Ste Marie | 1 | 0.00418 |
| Sault Ste. Marie | 1 | 0.00418 |
| Sausalito | 1 | 0.00418 |
| Saxonburg | 1 | 0.00418 |
| SC | 1 | 0.00418 |
| Schaumburg (Chicago Metro Area) | 1 | 0.00418 |
| Schenectady, NY | 1 | 0.00418 |
| Schiphol Rijk | 1 | 0.00418 |
| Scituate | 1 | 0.00418 |
| Scotch Plains | 1 | 0.00418 |
| Scotland | 1 | 0.00418 |
| Scotland, Ont | 1 | 0.00418 |
| Scott Depot | 1 | 0.00418 |
| Scotts Valley | 1 | 0.00418 |
| Scottsbluff | 1 | 0.00418 |
| Scranton/Wilkes-Barre | 1 | 0.00418 |
| Scunthorpe | 1 | 0.00418 |
| SeaTac | 1 | 0.00418 |
| Seatle, WA | 1 | 0.00418 |
| Seatlle | 1 | 0.00418 |
| Seattle (but fully remote) | 1 | 0.00418 |
| Seattle (company is in Redmond) | 1 | 0.00418 |
| Seattle metro | 1 | 0.00418 |
| Seattle Metro | 1 | 0.00418 |
| Seattle, wa | 1 | 0.00418 |
| Seattle, Wa | 1 | 0.00418 |
| Seattle, Washington | 1 | 0.00418 |
| Sebastopol | 1 | 0.00418 |
| Sebring | 1 | 0.00418 |
| Secaucus | 1 | 0.00418 |
| Secaucus NJ | 1 | 0.00418 |
| See above | 1 | 0.00418 |
| Seekonk | 1 | 0.00418 |
| semirural city | 1 | 0.00418 |
| Seneca | 1 | 0.00418 |
| Sequim | 1 | 0.00418 |
| Seven Hills, but really Remote | 1 | 0.00418 |
| Sewaren | 1 | 0.00418 |
| Sewickley | 1 | 0.00418 |
| Seymour | 1 | 0.00418 |
| sf | 1 | 0.00418 |
| Sf | 1 | 0.00418 |
| SF Bay | 1 | 0.00418 |
| Shaker Heights | 1 | 0.00418 |
| Shaker Hts | 1 | 0.00418 |
| Shakopee | 1 | 0.00418 |
| Shanghai | 1 | 0.00418 |
| Sharon | 1 | 0.00418 |
| Shawnee Mission | 1 | 0.00418 |
| Sheboygan, WI | 1 | 0.00418 |
| Shelby | 1 | 0.00418 |
| Shelter Island Heights | 1 | 0.00418 |
| Sherborne | 1 | 0.00418 |
| Sherbrooke | 1 | 0.00418 |
| Sherman | 1 | 0.00418 |
| Sherman Texas | 1 | 0.00418 |
| Shingle Springs | 1 | 0.00418 |
| SHIRLEY | 1 | 0.00418 |
| Shoreham-by-Sea | 1 | 0.00418 |
| Shoreline | 1 | 0.00418 |
| Shorewood | 1 | 0.00418 |
| Shrewsbury, VT | 1 | 0.00418 |
| Shropshire | 1 | 0.00418 |
| Sidney | 1 | 0.00418 |
| Siloam Springs | 1 | 0.00418 |
| silver spring | 1 | 0.00418 |
| Silver spring | 1 | 0.00418 |
| Silver Spring, Maryland | 1 | 0.00418 |
| Silver Spring, MD | 1 | 0.00418 |
| Silver Spriung | 1 | 0.00418 |
| Silverton | 1 | 0.00418 |
| Simsbury | 1 | 0.00418 |
| singapore | 1 | 0.00418 |
| Singleton | 1 | 0.00418 |
| Sint-Niklaas | 1 | 0.00418 |
| Sioux City | 1 | 0.00418 |
| Skillman | 1 | 0.00418 |
| Skip | 1 | 0.00418 |
| Skipton | 1 | 0.00418 |
| Skokie | 1 | 0.00418 |
| Skowhegan | 1 | 0.00418 |
| Slave lake | 1 | 0.00418 |
| SLC | 1 | 0.00418 |
| Sliedrecht | 1 | 0.00418 |
| Slough | 1 | 0.00418 |
| Small Canadian City | 1 | 0.00418 |
| Small city | 1 | 0.00418 |
| Small City | 1 | 0.00418 |
| Small city in SW UK | 1 | 0.00418 |
| Small city--actual city would give away the company | 1 | 0.00418 |
| Small coastal town | 1 | 0.00418 |
| small college town | 1 | 0.00418 |
| Small country, prefer not to say! | 1 | 0.00418 |
| Small Rural Town. Would no longer be anonymous if stated. | 1 | 0.00418 |
| Small state - no answer | 1 | 0.00418 |
| Small town called Reigate, in Surrey just outside London | 1 | 0.00418 |
| Small town Ohio | 1 | 0.00418 |
| Small Town Ohio | 1 | 0.00418 |
| Small town, Cornwall | 1 | 0.00418 |
| Smithers | 1 | 0.00418 |
| Smiths Station | 1 | 0.00418 |
| Smyrna | 1 | 0.00418 |
| Snohomish | 1 | 0.00418 |
| Snoqualmie | 1 | 0.00418 |
| SoCal, prefer not to specify | 1 | 0.00418 |
| Somerset County | 1 | 0.00418 |
| Somerville /Cambridge | 1 | 0.00418 |
| Sonoma | 1 | 0.00418 |
| Sonoma County | 1 | 0.00418 |
| Souderton | 1 | 0.00418 |
| South | 1 | 0.00418 |
| South bend | 1 | 0.00418 |
| south central AK | 1 | 0.00418 |
| South Charleston | 1 | 0.00418 |
| South Elgin | 1 | 0.00418 |
| south jordan | 1 | 0.00418 |
| South Molton | 1 | 0.00418 |
| South Orange | 1 | 0.00418 |
| South portland | 1 | 0.00418 |
| South Texas | 1 | 0.00418 |
| South Yarmouth | 1 | 0.00418 |
| Southeast WI | 1 | 0.00418 |
| SOUTHEND-ON-SEA | 1 | 0.00418 |
| Southern CA | 1 | 0.00418 |
| Southern Denmark | 1 | 0.00418 |
| southern IL | 1 | 0.00418 |
| Southern Maine | 1 | 0.00418 |
| Southern Maryland | 1 | 0.00418 |
| Southern MN | 1 | 0.00418 |
| Southington | 1 | 0.00418 |
| Southwest MI | 1 | 0.00418 |
| Sparks | 1 | 0.00418 |
| Spoka | 1 | 0.00418 |
| Spring | 1 | 0.00418 |
| Springdale | 1 | 0.00418 |
| Springfield, IL | 1 | 0.00418 |
| Springfield, ma | 1 | 0.00418 |
| Springfield, MA | 1 | 0.00418 |
| St Andrews, Scotland | 1 | 0.00418 |
| St Anne's | 1 | 0.00418 |
| St Clair Shores | 1 | 0.00418 |
| St joe | 1 | 0.00418 |
| St John's | 1 | 0.00418 |
| St John's, NL | 1 | 0.00418 |
| St John’s | 1 | 0.00418 |
| st louis | 1 | 0.00418 |
| St Louis, MO | 1 | 0.00418 |
| St Nazaire | 1 | 0.00418 |
| St pete beach | 1 | 0.00418 |
| St Petersburg | 1 | 0.00418 |
| St Thomas | 1 | 0.00418 |
| St. Catharines | 1 | 0.00418 |
| St. Charles | 1 | 0.00418 |
| St. Clair County | 1 | 0.00418 |
| St. Croix Falls | 1 | 0.00418 |
| St. Davids | 1 | 0.00418 |
| St. George | 1 | 0.00418 |
| St. John's, NL | 1 | 0.00418 |
| St. John’s | 1 | 0.00418 |
| St. Johns | 1 | 0.00418 |
| St. Joseph | 1 | 0.00418 |
| St. louis | 1 | 0.00418 |
| St. Louis adjacent | 1 | 0.00418 |
| St. Louis Park | 1 | 0.00418 |
| St. Paul/Minniapolis area | 1 | 0.00418 |
| St. Simons Island | 1 | 0.00418 |
| Stamford, CT | 1 | 0.00418 |
| Stanford, CA | 1 | 0.00418 |
| Stanwood | 1 | 0.00418 |
| State COllege | 1 | 0.00418 |
| Staten Island | 1 | 0.00418 |
| Statesboro | 1 | 0.00418 |
| Steamboat Springs | 1 | 0.00418 |
| Steinbach | 1 | 0.00418 |
| Steinbach, Manitoba | 1 | 0.00418 |
| Sterling Heights | 1 | 0.00418 |
| Stevenage | 1 | 0.00418 |
| Stillwater: WI based company, office in MN | 1 | 0.00418 |
| STL | 1 | 0.00418 |
| Stockholm. But also from Italy (online work). Or anywhere actually. | 1 | 0.00418 |
| Stockton, NJ | 1 | 0.00418 |
| Stoke | 1 | 0.00418 |
| Stoke on Trent | 1 | 0.00418 |
| Stone Mountain | 1 | 0.00418 |
| Stone, Staffordshire | 1 | 0.00418 |
| Stoughton, MA | 1 | 0.00418 |
| Strasbourg | 1 | 0.00418 |
| Stratford upon avon | 1 | 0.00418 |
| Stratford upon Avon | 1 | 0.00418 |
| Stratham | 1 | 0.00418 |
| Streamwood | 1 | 0.00418 |
| Sturbridge | 1 | 0.00418 |
| Sturtevant | 1 | 0.00418 |
| Suburb city in Metro NY area | 1 | 0.00418 |
| Suburb of Indianapolis | 1 | 0.00418 |
| Suburb of Philadelphia | 1 | 0.00418 |
| Suburban Chicago, Illinois | 1 | 0.00418 |
| Suburban Deteoit | 1 | 0.00418 |
| Suburban NYC Metro Area | 1 | 0.00418 |
| Suburban Philadelphia | 1 | 0.00418 |
| Suburban School District | 1 | 0.00418 |
| Suffolk County | 1 | 0.00418 |
| Sugar land | 1 | 0.00418 |
| Summerville | 1 | 0.00418 |
| Sumnyvale | 1 | 0.00418 |
| Sumter | 1 | 0.00418 |
| Sun Prairie | 1 | 0.00418 |
| Sun Valley | 1 | 0.00418 |
| Sunshine Coast | 1 | 0.00418 |
| Superior | 1 | 0.00418 |
| Surrey BC | 1 | 0.00418 |
| Surrey, BC | 1 | 0.00418 |
| Surrey, British Columbia | 1 | 0.00418 |
| Surrey, British Columbia, Canada | 1 | 0.00418 |
| Sutherlin | 1 | 0.00418 |
| Sutton (Surrey) | 1 | 0.00418 |
| SW MO - near Joplin | 1 | 0.00418 |
| Swampscott | 1 | 0.00418 |
| Sweet Springs | 1 | 0.00418 |
| Swift Current | 1 | 0.00418 |
| Swiftwater | 1 | 0.00418 |
| Sydney, NSW, Australia | 1 | 0.00418 |
| syracuse | 1 | 0.00418 |
| TACOMA | 1 | 0.00418 |
| Tacoma (we are now permanently WFH, but our office was based in Tacoma) | 1 | 0.00418 |
| Tahoe City | 1 | 0.00418 |
| Takoma Park, MD | 1 | 0.00418 |
| Tallahassee, FL | 1 | 0.00418 |
| Tallahssee | 1 | 0.00418 |
| Tallmadge | 1 | 0.00418 |
| Tampa Bay | 1 | 0.00418 |
| Tampa Bay area | 1 | 0.00418 |
| Tampa Bay region | 1 | 0.00418 |
| Tarrytown, NY | 1 | 0.00418 |
| Taunton | 1 | 0.00418 |
| Taylorsville | 1 | 0.00418 |
| Te Whanganui-a-Tara Wellington | 1 | 0.00418 |
| Tehachapi | 1 | 0.00418 |
| Tel aviv | 1 | 0.00418 |
| Telecommute | 1 | 0.00418 |
| Telecommute from Anaheim, CA; employer office in Rancho Cucamonga, CA | 1 | 0.00418 |
| Telecommute from small town | 1 | 0.00418 |
| Telework | 1 | 0.00418 |
| Telford | 1 | 0.00418 |
| Tell city | 1 | 0.00418 |
| Temecula | 1 | 0.00418 |
| Tempe, AZ | 1 | 0.00418 |
| Tennant Creek | 1 | 0.00418 |
| Tennessee | 1 | 0.00418 |
| Teramo | 1 | 0.00418 |
| Terrell | 1 | 0.00418 |
| Tewksbury | 1 | 0.00418 |
| Texas | 1 | 0.00418 |
| Texas City | 1 | 0.00418 |
| Thame | 1 | 0.00418 |
| Thayne | 1 | 0.00418 |
| The Dalles, Oregon | 1 | 0.00418 |
| The hague | 1 | 0.00418 |
| The Hague area | 1 | 0.00418 |
| the office is in Schaumburg; I work remotely | 1 | 0.00418 |
| The Pas Manitoba | 1 | 0.00418 |
| This answer would reveal my identity. | 1 | 0.00418 |
| This identifies my employer | 1 | 0.00418 |
| This info will make me identifiable | 1 | 0.00418 |
| This is too identifying | 1 | 0.00418 |
| This would definitely reveal my identity | 1 | 0.00418 |
| This would help identify me | 1 | 0.00418 |
| This would narrow things down too much, sorry! | 1 | 0.00418 |
| This would probably out me | 1 | 0.00418 |
| Thorofare | 1 | 0.00418 |
| Thousand oaks | 1 | 0.00418 |
| Three Rivers | 1 | 0.00418 |
| Tifton | 1 | 0.00418 |
| Tilton | 1 | 0.00418 |
| Timaru | 1 | 0.00418 |
| Tinley Park | 1 | 0.00418 |
| Tinton Falls | 1 | 0.00418 |
| Tishomingo | 1 | 0.00418 |
| Tiverton (Remote) but company based in Washington | 1 | 0.00418 |
| To Much Detail | 1 | 0.00418 |
| toledo | 1 | 0.00418 |
| Too identifiable | 1 | 0.00418 |
| Too Small | 1 | 0.00418 |
| too small to answer with anonymity! | 1 | 0.00418 |
| Too small to list | 1 | 0.00418 |
| Tooeka | 1 | 0.00418 |
| Torino | 1 | 0.00418 |
| Tornto | 1 | 0.00418 |
| Toronto ON | 1 | 0.00418 |
| Toronto Ontario Canada | 1 | 0.00418 |
| Toronto, for a global remote company | 1 | 0.00418 |
| Toronto, ON, CA | 1 | 0.00418 |
| Toronto, ontario | 1 | 0.00418 |
| Toronto, Ontario, Canada | 1 | 0.00418 |
| Torontoo | 1 | 0.00418 |
| Torquay, England | 1 | 0.00418 |
| Torrance, CA | 1 | 0.00418 |
| Torre pacheco | 1 | 0.00418 |
| Toulouse | 1 | 0.00418 |
| Traveling | 1 | 0.00418 |
| Traveling over all of Europe | 1 | 0.00418 |
| Trinidad | 1 | 0.00418 |
| Trondheim | 1 | 0.00418 |
| Truth or Consequences | 1 | 0.00418 |
| tualatin | 1 | 0.00418 |
| Tucker | 1 | 0.00418 |
| tucson | 1 | 0.00418 |
| Tucson, AZ | 1 | 0.00418 |
| Tucson, but I'm remote to a CO company | 1 | 0.00418 |
| Tukwila | 1 | 0.00418 |
| Tullahoma | 1 | 0.00418 |
| Tuolumne | 1 | 0.00418 |
| Turin | 1 | 0.00418 |
| Turin/Torino | 1 | 0.00418 |
| Turku | 1 | 0.00418 |
| Tustin but represent Orange County | 1 | 0.00418 |
| Twin cities | 1 | 0.00418 |
| Twin Cities (state wide org) | 1 | 0.00418 |
| Twin Cities Area | 1 | 0.00418 |
| Twin cities metro area | 1 | 0.00418 |
| Twin Cities metro area | 1 | 0.00418 |
| Twin Cities Metro Area | 1 | 0.00418 |
| Tyson's Corner | 1 | 0.00418 |
| Tysons corner | 1 | 0.00418 |
| Tysons Corner | 1 | 0.00418 |
| Uckfield | 1 | 0.00418 |
| Uedem | 1 | 0.00418 |
| Uintah | 1 | 0.00418 |
| uk | 1 | 0.00418 |
| Uk | 1 | 0.00418 |
| Ukiah | 1 | 0.00418 |
| Ullin | 1 | 0.00418 |
| Unicoi | 1 | 0.00418 |
| Union | 1 | 0.00418 |
| Union, NJ | 1 | 0.00418 |
| Uniondale | 1 | 0.00418 |
| United Kingdom | 1 | 0.00418 |
| Universal city | 1 | 0.00418 |
| University City | 1 | 0.00418 |
| University Park | 1 | 0.00418 |
| Upper Penninsula | 1 | 0.00418 |
| Upper valley | 1 | 0.00418 |
| Upper Valley | 1 | 0.00418 |
| Uppsala | 1 | 0.00418 |
| upstate | 1 | 0.00418 |
| Upstate | 1 | 0.00418 |
| Upstate New York | 1 | 0.00418 |
| Upstate region | 1 | 0.00418 |
| Upstate, NY | 1 | 0.00418 |
| US govt employee overseas, country withheld | 1 | 0.00418 |
| USA | 1 | 0.00418 |
| Utah | 1 | 0.00418 |
| Utah County (I cover multiple cities) | 1 | 0.00418 |
| Uusimaa | 1 | 0.00418 |
| Uxbridge | 1 | 0.00418 |
| Vai | 1 | 0.00418 |
| Vail | 1 | 0.00418 |
| Valletta | 1 | 0.00418 |
| Vancouver area | 1 | 0.00418 |
| Vancouver, BC | 1 | 0.00418 |
| Vancouver, B.C. | 1 | 0.00418 |
| Vancouver, Canada | 1 | 0.00418 |
| Vancouver, WA | 1 | 0.00418 |
| Varies | 1 | 0.00418 |
| Variety | 1 | 0.00418 |
| Vega | 1 | 0.00418 |
| Venice | 1 | 0.00418 |
| Vernal | 1 | 0.00418 |
| Vernon - Remote position though | 1 | 0.00418 |
| Vernon hills | 1 | 0.00418 |
| VERONA | 1 | 0.00418 |
| Verysmall town | 1 | 0.00418 |
| victoria | 1 | 0.00418 |
| Victoria BC | 1 | 0.00418 |
| Victoria, B.C., Canada | 1 | 0.00418 |
| Victoria, BC, Canada | 1 | 0.00418 |
| Vienna, VA | 1 | 0.00418 |
| Villa Park | 1 | 0.00418 |
| village near Riga | 1 | 0.00418 |
| Vinalhaven | 1 | 0.00418 |
| Vincennes | 1 | 0.00418 |
| Vineland | 1 | 0.00418 |
| Vineland, NJ | 1 | 0.00418 |
| Virgina Beach | 1 | 0.00418 |
| Viroqua | 1 | 0.00418 |
| Virtual role | 1 | 0.00418 |
| Virtual Worker | 1 | 0.00418 |
| WA | 1 | 0.00418 |
| Waddell | 1 | 0.00418 |
| Waikato | 1 | 0.00418 |
| Wailuku | 1 | 0.00418 |
| Waitsfield | 1 | 0.00418 |
| Wake Forest | 1 | 0.00418 |
| walla walla | 1 | 0.00418 |
| Wallingford | 1 | 0.00418 |
| Walnut Creek (SF Bay Area) | 1 | 0.00418 |
| Walnut Creek, California | 1 | 0.00418 |
| Walpole | 1 | 0.00418 |
| Waltham (suburb of Boston) | 1 | 0.00418 |
| Waltham, MA | 1 | 0.00418 |
| Warren, NJ | 1 | 0.00418 |
| Warrenton | 1 | 0.00418 |
| Warwick, RI | 1 | 0.00418 |
| WASHINGTON | 1 | 0.00418 |
| washington DC | 1 | 0.00418 |
| Washington DC (and Philadelphia PA) | 1 | 0.00418 |
| Washington DC metro area | 1 | 0.00418 |
| Washington state | 1 | 0.00418 |
| Washington, DC | 1 | 0.00418 |
| Washington, d.c. | 1 | 0.00418 |
| Washington, dc | 1 | 0.00418 |
| Washington, DC - Baltimore, MD area. | 1 | 0.00418 |
| Washington, DC (I'm remote, but office is in DC) | 1 | 0.00418 |
| Washington, DC area | 1 | 0.00418 |
| Washington, DC Primarily | 1 | 0.00418 |
| Washington, DC. | 1 | 0.00418 |
| Washington, District of Columbia | 1 | 0.00418 |
| WashingtonDC | 1 | 0.00418 |
| Wasington | 1 | 0.00418 |
| Waskesiu Lake | 1 | 0.00418 |
| Waterbury | 1 | 0.00418 |
| WATERBURY | 1 | 0.00418 |
| Waterbury, VT | 1 | 0.00418 |
| Waterford, MI | 1 | 0.00418 |
| Waterloo, ON, Canada | 1 | 0.00418 |
| Watertown (Greater Boston area) | 1 | 0.00418 |
| Watertown, MA | 1 | 0.00418 |
| Watsonville | 1 | 0.00418 |
| Wauconda | 1 | 0.00418 |
| Waukegan | 1 | 0.00418 |
| Wausau | 1 | 0.00418 |
| Wauseon | 1 | 0.00418 |
| Waverly | 1 | 0.00418 |
| Wayland | 1 | 0.00418 |
| Wayne, PA | 1 | 0.00418 |
| waynesboro | 1 | 0.00418 |
| Weaverville | 1 | 0.00418 |
| Weed, CA | 1 | 0.00418 |
| Weert | 1 | 0.00418 |
| Welland | 1 | 0.00418 |
| Wellesley | 1 | 0.00418 |
| Wellsboro | 1 | 0.00418 |
| Welwyn Garden City | 1 | 0.00418 |
| Wentzville | 1 | 0.00418 |
| Wentzville but I am remote | 1 | 0.00418 |
| West Babylon | 1 | 0.00418 |
| West Bridgewater | 1 | 0.00418 |
| West Cola | 1 | 0.00418 |
| West Conshohocken | 1 | 0.00418 |
| West England | 1 | 0.00418 |
| West Greenwich | 1 | 0.00418 |
| West Grove | 1 | 0.00418 |
| West Hollywood | 1 | 0.00418 |
| West Mifflin | 1 | 0.00418 |
| West Orange | 1 | 0.00418 |
| West Palm Beaxh | 1 | 0.00418 |
| West Springfield | 1 | 0.00418 |
| West St Paul | 1 | 0.00418 |
| West St. Paul | 1 | 0.00418 |
| West Sussex | 1 | 0.00418 |
| west TN | 1 | 0.00418 |
| West TN | 1 | 0.00418 |
| West Warwick | 1 | 0.00418 |
| Westampton | 1 | 0.00418 |
| Westchester/NYC | 1 | 0.00418 |
| Westerly | 1 | 0.00418 |
| Western Canada | 1 | 0.00418 |
| Western KS | 1 | 0.00418 |
| Western MA | 1 | 0.00418 |
| Western Mass | 1 | 0.00418 |
| Western MD | 1 | 0.00418 |
| Western Slope | 1 | 0.00418 |
| Westerville | 1 | 0.00418 |
| Westford | 1 | 0.00418 |
| Westlake Village, CA | 1 | 0.00418 |
| Westport | 1 | 0.00418 |
| WFH (Was Topeka, KS. Now Whiting, KS) | 1 | 0.00418 |
| WFH FT | 1 | 0.00418 |
| WFH in Northern NJ but company HQ is in Illinois, local office NYC | 1 | 0.00418 |
| WFH in San Francisco Bay Area | 1 | 0.00418 |
| WFH Kenilworth | 1 | 0.00418 |
| WFH remotely, employer in another state | 1 | 0.00418 |
| WFH. Job located in Newport, South Wales | 1 | 0.00418 |
| Wheatfield | 1 | 0.00418 |
| Whistler | 1 | 0.00418 |
| Whistler, BC | 1 | 0.00418 |
| White Earth | 1 | 0.00418 |
| White Marsh | 1 | 0.00418 |
| White Oak | 1 | 0.00418 |
| White River Junction, VT | 1 | 0.00418 |
| Whitefish Bay | 1 | 0.00418 |
| Whitehorse, YT | 1 | 0.00418 |
| Wichita (remotely) | 1 | 0.00418 |
| Wichita Falls | 1 | 0.00418 |
| Wilbraham | 1 | 0.00418 |
| Wilkes-Barre | 1 | 0.00418 |
| Willamette Valley Area | 1 | 0.00418 |
| Williamsburg, VA | 1 | 0.00418 |
| Williamsport | 1 | 0.00418 |
| Williamston | 1 | 0.00418 |
| Williamstown | 1 | 0.00418 |
| Willimantic | 1 | 0.00418 |
| Williston | 1 | 0.00418 |
| Willowbrook | 1 | 0.00418 |
| wilmington | 1 | 0.00418 |
| wilmington, nc | 1 | 0.00418 |
| Wilmington/ WFH | 1 | 0.00418 |
| Wilmslow | 1 | 0.00418 |
| Wilton, Connecticut | 1 | 0.00418 |
| Wiltshire | 1 | 0.00418 |
| Winchester, UK | 1 | 0.00418 |
| Windham | 1 | 0.00418 |
| Windsor Ontario | 1 | 0.00418 |
| Winkler | 1 | 0.00418 |
| Winter Park | 1 | 0.00418 |
| Winton | 1 | 0.00418 |
| Wisconsin | 1 | 0.00418 |
| Witchita | 1 | 0.00418 |
| Wixom | 1 | 0.00418 |
| Woburn (Greater Boston area; I provide services in a number of towns.) | 1 | 0.00418 |
| Woking | 1 | 0.00418 |
| Wokingham | 1 | 0.00418 |
| Wollongong | 1 | 0.00418 |
| Wolverhampton | 1 | 0.00418 |
| Wood Dale | 1 | 0.00418 |
| woodbridge | 1 | 0.00418 |
| Woodbridge | 1 | 0.00418 |
| Woodhaven | 1 | 0.00418 |
| Woodland | 1 | 0.00418 |
| Woodland Hills | 1 | 0.00418 |
| Woods Hole | 1 | 0.00418 |
| Woonsocket | 1 | 0.00418 |
| Wooster | 1 | 0.00418 |
| Worcester area | 1 | 0.00418 |
| Work across the whole state | 1 | 0.00418 |
| Work from Home | 1 | 0.00418 |
| Work From Home | 1 | 0.00418 |
| Work from home, London | 1 | 0.00418 |
| Work remotely for a NYC business from Portland, ME | 1 | 0.00418 |
| Work remotely, but the company is based out of Chicago, IL and I work in Newark, NJ | 1 | 0.00418 |
| Worthington | 1 | 0.00418 |
| would be identifying sorry | 1 | 0.00418 |
| would give too much away | 1 | 0.00418 |
| Would rather not answer ( would be very easy to figure out who i am) | 1 | 0.00418 |
| would rather not say | 1 | 0.00418 |
| would remove my anonymity to state this | 1 | 0.00418 |
| Wrexham | 1 | 0.00418 |
| Wright | 1 | 0.00418 |
| Würzburg | 1 | 0.00418 |
| Wyandanch | 1 | 0.00418 |
| Wyandotte | 1 | 0.00418 |
| Yakima | 1 | 0.00418 |
| Yakima but my employer is based in Seattle | 1 | 0.00418 |
| Yankton | 1 | 0.00418 |
| Yellowknife, NT | 1 | 0.00418 |
| Yeovil | 1 | 0.00418 |
| Yonkers | 1 | 0.00418 |
| York Region | 1 | 0.00418 |
| Yorkshire | 1 | 0.00418 |
| Youngstown | 1 | 0.00418 |
| Zachary | 1 | 0.00418 |
| Zagreb | 1 | 0.00418 |
| Zion | 1 | 0.00418 |
| Zurich | 1 | 0.00418 |
# education
ask_a_manager_2021 %>%
count(education) %>%
mutate(percent = 100* n/sum(n))%>%
arrange(desc(percent))
| education | n | percent |
|---|---|---|
| College degree | 11665 | 48.7 |
| Master's degree | 7811 | 32.6 |
| Some college | 1707 | 7.13 |
| PhD | 1228 | 5.13 |
| Professional degree (MD, JD, etc.) | 1011 | 4.22 |
| High School | 507 | 2.12 |
# gender
ask_a_manager_2021 %>%
count(gender) %>%
mutate(percent = 100* n/sum(n))%>%
arrange(desc(percent))
| gender | n | percent |
|---|---|---|
| Woman | 19626 | 82 |
| Man | 4303 | 18 |
# race
ask_a_manager_2021 %>%
count(race) %>%
mutate(percent = 100* n/sum(n))%>%
arrange(desc(percent))
| race | n | percent |
|---|---|---|
| White | 20262 | 84.7 |
| Asian or Asian American | 1087 | 4.54 |
| Black or African American | 578 | 2.42 |
| Hispanic, Latino, or Spanish origin | 478 | 2 |
| Another option not listed here or prefer not to answer | 403 | 1.68 |
| Hispanic, Latino, or Spanish origin, White | 325 | 1.36 |
| Asian or Asian American, White | 293 | 1.22 |
| Black or African American, White | 108 | 0.451 |
| Middle Eastern or Northern African, White | 61 | 0.255 |
| Native American or Alaska Native, White | 60 | 0.251 |
| White, Another option not listed here or prefer not to answer | 51 | 0.213 |
| Middle Eastern or Northern African | 50 | 0.209 |
| Native American or Alaska Native | 34 | 0.142 |
| Black or African American, Hispanic, Latino, or Spanish origin | 24 | 0.1 |
| Asian or Asian American, Hispanic, Latino, or Spanish origin | 10 | 0.0418 |
| Asian or Asian American, Hispanic, Latino, or Spanish origin, White | 9 | 0.0376 |
| Hispanic, Latino, or Spanish origin, Native American or Alaska Native | 9 | 0.0376 |
| Asian or Asian American, Another option not listed here or prefer not to answer | 8 | 0.0334 |
| Black or African American, Hispanic, Latino, or Spanish origin, White | 8 | 0.0334 |
| Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White | 8 | 0.0334 |
| Asian or Asian American, Black or African American | 7 | 0.0293 |
| Asian or Asian American, White, Another option not listed here or prefer not to answer | 6 | 0.0251 |
| Hispanic, Latino, or Spanish origin, Another option not listed here or prefer not to answer | 6 | 0.0251 |
| Asian or Asian American, Black or African American, White | 5 | 0.0209 |
| Asian or Asian American, Middle Eastern or Northern African | 5 | 0.0209 |
| Black or African American, Native American or Alaska Native, White | 5 | 0.0209 |
| Asian or Asian American, Native American or Alaska Native, White | 4 | 0.0167 |
| Black or African American, Middle Eastern or Northern African, White | 4 | 0.0167 |
| Hispanic, Latino, or Spanish origin, Middle Eastern or Northern African, White | 3 | 0.0125 |
| Black or African American, Another option not listed here or prefer not to answer | 2 | 0.00836 |
| Black or African American, Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White | 2 | 0.00836 |
| Black or African American, Middle Eastern or Northern African | 2 | 0.00836 |
| Middle Eastern or Northern African, White, Another option not listed here or prefer not to answer | 2 | 0.00836 |
| Asian or Asian American, Hispanic, Latino, or Spanish origin, Another option not listed here or prefer not to answer | 1 | 0.00418 |
| Asian or Asian American, Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White | 1 | 0.00418 |
| Asian or Asian American, Middle Eastern or Northern African, White | 1 | 0.00418 |
| Black or African American, Middle Eastern or Northern African, Native American or Alaska Native, White | 1 | 0.00418 |
| Hispanic, Latino, or Spanish origin, Middle Eastern or Northern African | 1 | 0.00418 |
| Hispanic, Latino, or Spanish origin, Native American or Alaska Native, Another option not listed here or prefer not to answer | 1 | 0.00418 |
| Middle Eastern or Northern African, Native American or Alaska Native | 1 | 0.00418 |
| Middle Eastern or Northern African, Native American or Alaska Native, White | 1 | 0.00418 |
| Native American or Alaska Native, Another option not listed here or prefer not to answer | 1 | 0.00418 |
| Native American or Alaska Native, White, Another option not listed here or prefer not to answer | 1 | 0.00418 |
# pro_exp
ask_a_manager_2021 %>%
count(pro_exp ) %>%
mutate(percent = 100* n/sum(n))%>%
arrange(desc(percent))
| pro_exp | n | percent |
|---|---|---|
| 11 - 20 years | 8332 | 34.8 |
| 8 - 10 years | 4627 | 19.3 |
| 5-7 years | 4128 | 17.3 |
| 21 - 30 years | 3123 | 13.1 |
| 2 - 4 years | 2473 | 10.3 |
| 31 - 40 years | 745 | 3.11 |
| 1 year or less | 392 | 1.64 |
| 41 years or more | 109 | 0.456 |
# field_exp
ask_a_manager_2021 %>%
count(field_exp) %>%
mutate(percent = 100* n/sum(n))%>%
arrange(desc(percent))
| field_exp | n | percent |
|---|---|---|
| 5-7 years | 5657 | 23.6 |
| 11 - 20 years | 5642 | 23.6 |
| 2 - 4 years | 5235 | 21.9 |
| 8 - 10 years | 4315 | 18 |
| 21 - 30 years | 1561 | 6.52 |
| 1 year or less | 1166 | 4.87 |
| 31 - 40 years | 319 | 1.33 |
| 41 years or more | 34 | 0.142 |
#make comments about the counts
How is salary distributed?
favstats(ask_a_manager_2021$salary) # find a really rich guy here
| min | Q1 | median | Q3 | max | mean | sd | n | missing | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 5.4e+04 | 7.5e+04 | 1.04e+05 | 1.92e+05 | 8.18e+04 | 3.75e+04 | 23929 | 0 | ||||||
| min | Q1 | median | Q3 | max | mean | sd | n | missing |
|---|---|---|---|---|---|---|---|---|
| 0 | 5.5e+04 | 7.6e+04 | 1.09e+05 | 5e+06 | 8.93e+04 | 7.07e+04 | 24899 | 0 |
# density
g1 <- ggplot(ask_a_manager_2021, aes(x=salary))+
geom_density()+
NULL
#cdf
g2 <- ask_a_manager_2021 %>%
slice_min(order_by = salary,n =nrow(ask_a_manager_2021)-5) %>% # choose a number
ggplot(aes(x=salary))+
stat_ecdf()+
NULL
# taking log is better
g3 <- ggplot(ask_a_manager_2021, aes(x=log(salary)))+
geom_density()+
NULL
g4 <- ask_a_manager_2021 %>%
slice_min(order_by = salary,n =nrow(ask_a_manager_2021)-5) %>% # choose a number
ggplot(aes(x=log(salary)))+
stat_ecdf()+
NULL
gridExtra::grid.arrange(g1,g2,g3,g4)
<<<<<<< HEAD
Observation: This is a right skewed distribution.
Observation: This is a right skewed distribution.
ask_a_manager_2021 %>%
count(industry, sort=TRUE) %>%
ggplot(aes(y=fct_reorder(industry,n), x=n))+
geom_col()
<<<<<<< HEAD
Observations:
We are interested in the type of industries with top salary for man and woman.
# Industries of top average salary for Man and Woman
salary_table <- ask_a_manager_2021 %>%
group_by(industry, gender) %>%
summarise(count = n(),
avg_salary = mean(converted_salary),
higher_edu_prop = sum(higher_edu)/n()) %>%
pivot_wider(id_cols = 1:5,
names_from = gender,
values_from = count:higher_edu_prop)
g_man <- salary_table %>%
ggplot(aes(y=fct_reorder(industry,avg_salary_Man), x=avg_salary_Man,fill=higher_edu_prop_Man))+
geom_bar(position="dodge", stat="identity")+
geom_text(aes(label=paste(avg_salary_Man%/%1000,"k")),
position=position_dodge(width=0.9), hjust=0,vjust=0)+
scale_x_continuous(labels = scales::comma) +
scale_fill_gradient(low="sky blue", high="blue")+
labs(
title="Man",
x="Annual Salary $",
y="Industry",
fill="Higher Education %"
)+
theme_wsj()+
theme(legend.position="bottom",
legend.title=element_text(size=13))+
NULL
<<<<<<< HEAD
ggsave("highest_paid_industries_male.png",width = 12,height = 8,limitsize = F)
=======
# ggsave("highest_paid_industries_male.png",width = 12,height = 8,limitsize = F)
>>>>>>> XuejingHuang-master
g_woman <- salary_table %>%
ggplot(aes(y=fct_reorder(industry,avg_salary_Woman), x=avg_salary_Woman,fill=higher_edu_prop_Woman))+
geom_bar( position="dodge", stat="identity")+
geom_text(aes(label=paste(avg_salary_Woman%/%1000,"k")),
position=position_dodge(width=0.9), hjust=0,vjust=0)+
scale_x_continuous(labels = scales::comma) +
scale_fill_gradient(low="pink", high="red")+
labs(
title="Woman",
x="Annual Salary $",
y="Industry",
fill="Higher Education %"
)+
theme_wsj()+
theme(legend.position="bottom",
legend.title=element_text(size=13))+
NULL
<<<<<<< HEAD
ggsave("highest_paid_industries_female.png",width = 12,height = 8,limitsize = F)
=======
# ggsave("highest_paid_industries_female.png",width = 12,height = 8,limitsize = F)
>>>>>>> XuejingHuang-master
Observations:
We are also interested in the salary difference between man and woman within the same industry.
# salary gap in different industries
ask_a_manager_2021 %>%
group_by(industry, gender) %>%
summarise(count = n(),
avg_salary = mean(converted_salary)) %>%
pivot_wider(id_cols = 1:4,
names_from = gender,
values_from = count:avg_salary) %>%
mutate(size = count_Man+count_Woman,
male_prop = count_Man/(count_Man+count_Woman),
salary_gap = avg_salary_Man - avg_salary_Woman) %>%
ggplot(aes(y=fct_reorder(industry,salary_gap), x=salary_gap,fill=male_prop))+
geom_col()+
# geom_point(aes(y=fct_reorder(industry,size),x=size*10))+
scale_x_continuous(
breaks = seq(-20000,40000,5000)
) +
scale_fill_gradient(low="pink", high="blue")+
labs(
title="Salary Gap and Sex Ratio",
x="Salary Gap in $",
y="",
fill="Male %"
)+
theme_wsj()+
theme(legend.position="bottom",
legend.title=element_text(size=13))+
NULL
<<<<<<< HEAD
ggsave("salary_gap.png",width = 12,height = 8,limitsize = F)
=======
# ggsave("salary_gap.png",width = 12,height = 8,limitsize = F)
>>>>>>> XuejingHuang-master
Observations:
Next, we want to see if there is a difference in the salary gap between man and woman for different education level.
ask_a_manager_2021 %>%
group_by(education, gender) %>%
summarise(count = n(),
avg_salary = mean(converted_salary)) %>%
pivot_wider(id_cols = 1:4,
names_from = gender,
values_from = count:avg_salary) %>%
mutate(size = count_Man+count_Woman,
male_prop = count_Man/(count_Man+count_Woman),
salary_gap = avg_salary_Man - avg_salary_Woman) %>%
ggplot(aes(x=education, y=salary_gap,fill=male_prop))+
geom_col()+
scale_y_continuous(
breaks = seq(-20000,40000,5000)
) +
scale_fill_gradient(low="pink", high="sky blue")+
theme_wsj()+
theme(legend.position="bottom", axis.title=element_text(size=24))+
ggtitle("Salary Gap at Different Education level") +
xlab("Education Level") + ylab ("Salary Gap in $") +
NULL
<<<<<<< HEAD
ggsave("salary_gap_by_edu.png",width = 14,height = 8,limitsize = F)
# ggsave("salary_gap_by_edu.png",width = 14,height = 8,limitsize = F)
ask_a_manager_2021 %>%
group_by(race) %>%
filter(n() > 500) %>%
summarise(count=n()) %>%
ggplot(aes(y=count, x=race, fill=race))+
geom_col() +
theme_wsj()+
theme(legend.position = "none") +
labs(x="Race", y="Count", title="Count by Race", subtitle = "Races with > 500 Respondents")+
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
NULL
<<<<<<< HEAD
ask_a_manager_2021 %>%
filter(industry %in% industry_list$industry) %>%
group_by(race) %>%
filter(n() > 500) %>%
ggplot( aes(x=race, y=converted_salary, color=race)) +
geom_jitter(color="black", size=0.01, alpha=0.9) +
geom_boxplot(alpha=0.4) +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_hline(yintercept=144988, color="red") +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
theme_wsj() +
theme(legend.position = "none")+
theme(axis.title=element_text(size=10))+
ggtitle("Salary Vs. Race Boxplot") +
ylab("Salary") + xlab ("Race") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))+
scale_y_continuous(labels = scales::comma, limits = c(0, 300000)) +
NULL
<<<<<<< HEAD
Observation: In our boxplot one can visualize both the distribution of the data and the medium and interquartile ranges of the data. When comparing annual salary by race, our data shows that Asians have the higher salaries in comparison to Black and African American and White sample respondents. Interestingly enough, our sample also shows that the medium salary for Black and AA respondents is higher than that of White respondents.
Since most repondents were white, the larger sample size is less prone to skewness due to outliers when comparing it to the other two races portrayed in the plot.
======= Observation:
In our boxplot one can visualize both the distribution of the data and the medium and interquartile ranges of the data. When comparing annual salary by race, our data shows that Asians have the higher salaries in comparison to Black and African American and White sample respondents. Interestingly enough, our sample also shows that the medium salary for Black and AA respondents is higher than that of White respondents.
Since most repondents were white, the larger sample size is less prone to skewness due to outliers when comparing it to the other two races portrayed in the plot.
ask_a_manager_2021_race <- ask_a_manager_2021 %>%
group_by(race) %>%
filter(race %in% c("Black or African American", "White"))
t.test(salary ~ race, data = ask_a_manager_2021_race)
##
## Welch Two Sample t-test
##
## data: salary by race
<<<<<<< HEAD
## t = 0.6, df = 610, p-value = 0.5
## alternative hypothesis: true difference in means between group Black or African American and group White is not equal to 0
## 95 percent confidence interval:
## -2140 4021
## sample estimates:
## mean in group Black or African American mean in group White
## 81976 81035
When running a T-test on the two racial groups, White and Black or African American, the P-value we get is 0.9. Thus there is no statistically signifcant difference in their annual salary when calculating it based of off this data set. ### Race and Mean Salary
#create df of the list of states with >10 occurrences
=======
## t = 1, df = 605, p-value = 0.3
## alternative hypothesis: true difference in means between group Black or African American and group White is not equal to 0
## 95 percent confidence interval:
## -6923 26250
## sample estimates:
## mean in group Black or African American mean in group White
## 97570 87906
When running a T-test on the two racial groups, White and Black or African American, the P-value we get is 0.9. Thus there is no statistically signifcant difference in their annual salary when calculating it based of off this data set.
#This package is useful for creating a map of the USA
library(usmap)
library(ggrepel)
#create df of the list of states with >10 occurrences
>>>>>>> XuejingHuang-master
#This allows us to get data for actual states as many data entry errors
#many individuals put multiple states and this helps to fix that
states <- ask_a_manager_2021 %>%
group_by(state) %>%
filter(!is.na(state)) %>%
filter(n()>10) %>%
summarise(mean_salary = mean(salary),
count_state = count(state))
states$fips <- fips(states$state)
statesalarymap <- states %>% select(state,mean_salary)
plot_usmap(data=statesalarymap, values="mean_salary", color="black") + scale_fill_continuous(
low = "white",
high = "light green",
name = "Mean Salary") +
theme(legend.position = "right") +
labs(title="Map of US States based on Average Salary")
<<<<<<< HEAD
MAP
# Count by Industry
ask_a_manager_2021 %>%
group_by(industry) %>%
summarise(count=n()) %>%
ggplot(aes(y=fct_reorder(industry,count),x=count))+
geom_col()
<<<<<<< HEAD
# null hypothesis: no difference between
=======
# null hypothesis: no difference between
ask_a_manager_2021 %>%
group_by(age_group, gender) %>%
summarise(count=n())
## # A tibble: 13 x 3
## # Groups: age_group [7]
## age_group gender count
## <fct> <chr> <int>
## 1 under 18 Woman 6
## 2 18-24 Man 168
## 3 18-24 Woman 759
## 4 25-34 Man 1853
## 5 25-34 Woman 9155
## 6 35-44 Man 1907
## 7 35-44 Woman 7110
## 8 45-54 Man 616
## 9 45-54 Woman 2327
## 10 55-64 Man 156
## 11 55-64 Woman 754
## 12 65 or over Man 17
## 13 65 or over Woman 71
#ggplot(aes(y=fct_reorder(how_old_are_you,count),x=count))+
#geom_col()
>>>>>>> XuejingHuang-master
# Count by Age Group
ask_a_manager_2021 %>%
group_by(age_group) %>%
summarise(count=n()) %>%
ggplot(aes(y=fct_reorder(age_group,count),x=count))+
geom_col()
<<<<<<< HEAD
# Sorting out the people who are 18+
ask_a_manager_age <- ask_a_manager_2021 %>%
mutate(factor(age_group,levels=c("18-24","25-34","35-44","45-54","55-64","65 or over")))
#need to change colors for male and female ?!
#there are only 6 women in under 18 category so excluding them for the graph
# since there is no male data in that category to compare
ask_a_manager_age %>%
filter(age_group %in% c("18-24", "25-34" , "35-44" , "45-54" , "55-64" )) %>%
group_by(age_group, gender) %>%
summarise (median_salary_by_age = median (salary),
mean_salary_by_age = mean(salary)) %>%
ggplot () +
geom_col(aes (age_group, median_salary_by_age, fill = gender), position = position_dodge(width = 0.9), alpha = 0.7)+
geom_point(aes(x = age_group, y=mean_salary_by_age, color = gender))+
theme_bw()+
#formatC(x, format = "d")+
labs( title = "Median Salary by Age Group" , y="Salary" ,x = "Age Group") +
NULL
<<<<<<< HEAD
ask_a_manager_2021_changed <- ask_a_manager_2021 %>%
mutate(if_changed= if_else(overall_years_of_professional_experience == years_of_experience_in_field, "No", "Yes"))
ask_a_manager_2021_changed %>%
ggplot(aes(x=fct_relevel(overall_years_of_professional_experience,levels=c("1 year or less","2 - 4 years","5-7 years","8 - 10 years","11 - 20 years","21 - 30 years","31 - 40 years","41 years or more")), y=annual_salary, color=if_changed))+
# scatter plot
geom_point(alpha = 0.5)+
# linear smooth line
geom_smooth(method = "lm")+
# legend settings
theme(legend.position = "bottom",
legend.title = element_blank())+
labs(title="Whether changing job affects your salary",
x="Total Years Spent Working",
y="Salary")+
theme_bw()+
NULL
ask_a_manager_2021_changed %>%
group_by(overall_years_of_professional_experience, if_changed) %>%
summarise (median_salary_over_time = median (salary),
mean_salary_over_time = mean(salary)) %>%
ggplot () +
geom_col(aes (fct_relevel(overall_years_of_professional_experience,levels=c("1 year or less","2 - 4 years","5-7 years","8 - 10 years","11 - 20 years","21 - 30 years","31 - 40 years","41 years or more")), median_salary_over_time, fill=if_changed), position = position_dodge(width = 0.9), alpha = 0.7)+
geom_point(aes(x = overall_years_of_professional_experience, y=mean_salary_over_time, color = if_changed))+
theme_bw()+
#formatC(x, format = "d")+
labs( title = "Median Salary over time" , y="Salary" ,x = "Prof") +
NULL
ggsave("switch_industry.png",width = 12,height = 8,limitsize = F)
>>>>>>> XuejingHuang-master
mosaic::favstats (converted_salary ~ gender, data=ask_a_manager_2021)
| gender | min | Q1 | median | Q3 | max | mean | sd | n | missing | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Man | 1 | 6.3e+04 | 9e+04 | 1.28e+05 | 2.62e+05 | 9.67e+04 | 4.3e+04 | 4303 | 0 | |||||
| Woman | 35 | 5.25e+04 | 7.11e+04 | 9.7e+04 | 2.55e+05 | 7.82e+04 | 3.48e+04 | 19626 | 0 |
| gender | min | Q1 | median | Q3 | max | mean | sd | n | missing |
|---|---|---|---|---|---|---|---|---|---|
| Man | 0 | 6.5e+04 | 9.6e+04 | 1.41e+05 | 2.11e+06 | 1.12e+05 | 8.33e+04 | 4717 | 0 |
| Woman | 0 | 5.3e+04 | 7.2e+04 | 1e+05 | 5e+06 | 8.38e+04 | 6.63e+04 | 20182 | 0 |
ask_a_manager_2021 %>%
ggplot( aes(x=gender, y=converted_salary, color=gender)) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
geom_boxplot(alpha=0.3) +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
theme_wsj() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
theme(axis.title=element_text(size=12))+
ggtitle("Salary Vs. Gender Boxplot with Jitter") +
ylab("Salary") + xlab ("Race") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))+
scale_y_continuous(labels = scales::comma, limits = c(0, 1000000)) +
NULL
<<<<<<< HEAD
For our following analysis, we will be comparing data from the male gender “Man” with data from the female gender “Woman.” We will filter out the data on those that are non binary.
ask_a_manager_2021 <- ask_a_manager_2021 %>%
filter(gender!="Non-binary")
t.test(converted_salary ~ gender, data = ask_a_manager_2021)
##
## Welch Two Sample t-test
##
## data: converted_salary by gender
<<<<<<< HEAD
## t = 26, df = 5602, p-value <2e-16
## alternative hypothesis: true difference in means between group Man and group Woman is not equal to 0
## 95 percent confidence interval:
## 17124 19871
## sample estimates:
## mean in group Man mean in group Woman
## 96721 78223
=======
## t = 21, df = 6183, p-value <2e-16
## alternative hypothesis: true difference in means between group Man and group Woman is not equal to 0
## 95 percent confidence interval:
## 25223 30318
## sample estimates:
## mean in group Man mean in group Woman
## 111551 83781
>>>>>>> XuejingHuang-master
#mosaic::favstats (annual_salary ~ gender, data=ask_a_manager_2021)
ask_a_manager_2021_gender <- ask_a_manager_2021 %>%
mutate(gender2 = case_when(
gender == "Man" ~ "1",
gender == "Woman" ~ "0",
TRUE ~ "NA"
)) %>%
mutate(gender2 = as.numeric(gender2))
mosaic::favstats (converted_salary ~ gender, data=ask_a_manager_2021_gender) %>%
mutate(t_critical = qt(0.975,n-1),
sd_mean = sd/sqrt(n),
margin_of_error = t_critical*sd_mean,
ci_lower = mean-margin_of_error,
ci_higher = mean+margin_of_error)
| gender | min | Q1 | median | Q3 | max | mean | sd | n | missing | t_critical | sd_mean | margin_of_error | ci_lower | ci_higher |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Man | 1 | 6.3e+04 | 9e+04 | 1.28e+05 | 2.62e+05 | 9.67e+04 | 4.3e+04 | 4303 | 0 | 1.96 | 655 | 1.28e+03 | 9.54e+04 | 9.8e+04 |
| Woman | 35 | 5.25e+04 | 7.11e+04 | 9.7e+04 | 2.55e+05 | 7.82e+04 | 3.48e+04 | 19626 | 0 | 1.96 | 248 | 487 | 7.77e+04 | 7.87e+04 |
# Add graph of two distributions and confidence intervals
Observation: Since two 95% confidence intervals do not overlap, we are at least 95% confident that the male have higher salary than the female on average.
=======# Add graph of two distributions and confidence intervals
Observation:
# hypothesis testing using infer package
diff <- ask_a_manager_2021_gender %>%
specify(converted_salary ~ gender) %>%
calculate(stat = "diff in means", order = c("Woman", "Man"))
set.seed(1234)
salary_in_null_world <- ask_a_manager_2021_gender %>%
filter(gender !="Non-binary") %>%
specify(converted_salary ~ gender) %>%
# assuming independence of gender and salary
hypothesize(null = "independence") %>%
# generate 1000 reps, of type "permute"
generate(reps = 1000, type = "permute") %>%
# calculate statistic of difference, namely "diff in means"
calculate(stat = "diff in means", order = c("Woman", "Man"))
salary_in_null_world %>%
visualise()+
shade_p_value(obs_stat = diff, direction = "two-sided")
<<<<<<< HEAD
p_value <- salary_in_null_world %>%
get_pvalue(obs_stat = diff, direction="both")
p_value
| p_value |
|---|
| 0 |
Observation:
We want to look at the relationship between one’s gender and education degree. In essence, we want to explore if there is a difference in the proportion of respondents with high school degree as their highest degree for male group and the female group. We can use the proportion test to generate the result from the male and female sample.
ask_a_manager_2021_education <- ask_a_manager_2021 %>%
filter(gender !="Non-binary") %>%
mutate(below_college = if_else(highest_level_of_education_completed %in% c("High School", "Some college"), "Yes", "No")) %>%
group_by(gender) %>%
summarise(college_below=sum(below_college == "Yes"), college_or_above = sum(below_college == "No"))
ask_a_manager_2021_education
| gender | college_below | college_or_above |
|---|---|---|
| Man | 635 | 3668 |
| Woman | 1579 | 18047 | Man | 687 | 4030 |
| Woman | 1598 | 18584 |
prop.test(x = c(4030,18584), n = c(4030+687,18584+1598), alternative = "two.sided")
##
## 2-sample test for equality of proportions with continuity correction
##
## data: c out of c4030 out of 4030 + 68718584 out of 18584 + 1598
## X-squared = 202, df = 1, p-value <2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.0773 -0.0556
## sample estimates:
## prop 1 prop 2
## 0.854 0.921
<<<<<<< HEAD
From the result of proportion test for male group and female group, we are 99% confident that the proportion of high school as highest education level for male is higher than that for female, indicating that overall female has a higher college-entry rate vs. the male.
=======Observations:
If we repeat the process to explore the difference in proportion in acquiring a masters or above degree for both gender:
ask_a_manager_2021_education <- ask_a_manager_2021 %>%
filter(gender !="Non-binary") %>%
mutate(higher_edu = if_else(highest_level_of_education_completed %in% c("Master's degree", "Professional degree (MD, JD, etc.)", "PhD"), "Yes", "No")) %>%
group_by(gender) %>%
summarise(master_below = sum(higher_edu == "No"), master_or_above=sum(higher_edu == "Yes") )
ask_a_manager_2021_education
| gender | master_below | master_or_above |
|---|---|---|
| Man | 2811 | 1492 |
| Woman | 11068 | 8558 | Man | 3063 | 1654 |
| Woman | 11260 | 8922 |
prop.test(x = c(1654, 8922), n = c(1654+3063, 8922+11260), alternative = "two.sided")
##
## 2-sample test for equality of proportions with continuity correction
##
## data: c out of c1654 out of 1654 + 30638922 out of 8922 + 11260
## X-squared = 130, df = 1, p-value <2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.1068 -0.0761
## sample estimates:
## prop 1 prop 2
## 0.351 0.442
<<<<<<< HEAD
From the result of two proportion tests, we can see that even though the proportion of acquiring a college-or-above degree is less in the male group, the proportion of acquiring higher education degree (Master’s-or-above) is higher than the female group. This somehow explains the fact that there are more outliers in male sample with extremely high income level.
Observations:
Here we want to see if race is affecting the salary.
ask_a_manager_salary <- ask_a_manager_2021 %>%
mutate(if_white = if_else(race == "White", "Yes", "No")) %>%
select(if_white,salary)
<<<<<<< HEAD
=======
>>>>>>> XuejingHuang-master
t.test(salary~if_white, data = ask_a_manager_salary, success = "Yes")
##
## Welch Two Sample t-test
##
## data: salary by if_white
<<<<<<< HEAD
## t = 7, df = 4948, p-value = 4e-12
## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
## 95 percent confidence interval:
## 3474 6195
## sample estimates:
## mean in group No mean in group Yes
## 85869 81035
wisely choose the race. like white vs. not white
We want to look at the relationship between one’s race and education degree. Particularly, we want to see if there is a difference in education level for those being white and those being non-white.
As the above analysis for gender vs. education, we run two proportion tests for college-or-above degree percentage and master-or-above percentage between the male and female group.
ask_a_manager_2021_race <- ask_a_manager_2021 %>%
mutate(if_white = if_else(race == "White", "White", "Non-white"),
below_college = if_else(highest_level_of_education_completed %in% c( "High School", "Some college"), "Yes", "No")) %>%
group_by(if_white) %>%
summarise(college_below=sum(below_college == "Yes"), college_or_above = sum(below_college == "No"))
ask_a_manager_2021_race
| if_white | college_below | college_or_above |
|---|---|---|
| Non-white | 334 | 3333 |
| White | 1880 | 18382 | Non-white | 350 | 3510 |
| White | 1935 | 19104 |
prop.test(x = c(3510,19104), n = c(3510+350, 19104+1935), alternative = "two.sided")
##
## 2-sample test for equality of proportions with continuity correction
##
## data: c out of c3510 out of 3510 + 35019104 out of 19104 + 1935
## X-squared = 0.05, df = 1, p-value = 0.8
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.00872 0.01132
## sample estimates:
## prop 1 prop 2
## 0.909 0.908
Similarly, if we run the test for the propotion of master_or_above education level between white and non-white races:
ask_a_manager_2021_race <- ask_a_manager_2021 %>%
mutate(if_white = if_else(race == "White", "White", "Non-white"),
higher_edu = if_else(highest_level_of_education_completed %in% c("Master's degree", "Professional degree (MD, JD, etc.)", "PhD"), "Yes", "No")) %>%
group_by(if_white) %>%
summarise(master_below = sum(higher_edu == "No"), master_or_above=sum(higher_edu == "Yes") )
ask_a_manager_2021_race
| if_white | master_below | master_or_above |
|---|---|---|
| Non-white | 2256 | 1411 |
| White | 11623 | 8639 | Non-white | 2347 | 1513 |
| White | 11976 | 9063 |
prop.test(x = c(1513, 9063), n = c(1513+2347, 9063+11976), alternative = "two.sided")
##
## 2-sample test for equality of proportions with continuity correction
##
## data: c out of c1513 out of 1513 + 23479063 out of 9063 + 11976
## X-squared = 20, df = 1, p-value = 8e-06
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.0557 -0.0219
## sample estimates:
## prop 1 prop 2
## 0.392 0.431
<<<<<<< HEAD
From the result of the proportion test, we cannot see a clear difference between the proportion of education level in white and non-white group, as the p-value is higher than 5%.
However, the proportion of acquiring higher education degree (Master’s-or-above) is higher in the white group vs. other races
ask_a_manager_2021_changed <- ask_a_manager_2021 %>%
mutate(if_changed= if_else(overall_years_of_professional_experience == years_of_experience_in_field, "No", "Yes"))
ask_a_manager_2021_changed %>%
ggplot(aes(x=fct_relevel(overall_years_of_professional_experience,levels=c("1 year or less","2 - 4 years","5-7 years","8 - 10 years","11 - 20 years","21 - 30 years","31 - 40 years","41 years or more")), y=annual_salary, color=if_changed))+
# scatter plot
geom_point(alpha = 0.5)+
# linear smooth line
geom_smooth(method = "lm")+
# legend settings
theme(legend.position = "bottom",
legend.title = element_blank())+
labs(title="Whether changing job affects your salary",
x="Total Years Spent Working",
y="Salary")+
theme_bw()+
NULL
ask_a_manager_2021_changed %>%
group_by(overall_years_of_professional_experience, if_changed) %>%
summarise (median_salary_over_time = median (salary),
mean_salary_over_time = mean(salary)) %>%
ggplot () +
geom_col(aes (fct_relevel(overall_years_of_professional_experience,levels=c("1 year or less","2 - 4 years","5-7 years","8 - 10 years","11 - 20 years","21 - 30 years","31 - 40 years","41 years or more")), median_salary_over_time, fill=if_changed), position = position_dodge(width = 0.9), alpha = 0.7)+
geom_point(aes(x = overall_years_of_professional_experience, y=mean_salary_over_time, color = if_changed))+
theme_bw()+
#formatC(x, format = "d")+
labs( title = "Median Salary over time" , y="Salary" ,x = "Prof") +
NULL
ggsave("switch_industry.png",width = 12,height = 8,limitsize = F)
=======
Observations:
From the result of the proportion test, we cannot see a clear difference between the proportion of education level in white and non-white group, as the p-value is higher than 5%.
However, the proportion of acquiring higher education degree (Master’s-or-above) is higher in the white group vs. other races
t.test(salary~if_changed, data = ask_a_manager_2021_changed)
##
## Welch Two Sample t-test
##
## data: salary by if_changed
<<<<<<< HEAD
## t = 19, df = 23826, p-value <2e-16
## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
## 95 percent confidence interval:
## 7949 9827
## sample estimates:
## mean in group No mean in group Yes
## 85884 76996
=======
## t = 15, df = 24238, p-value <2e-16
## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
## 95 percent confidence interval:
## 11569 15074
## sample estimates:
## mean in group No mean in group Yes
## 95369 82047
>>>>>>> XuejingHuang-master
prop.test(~if_changed, data = ask_a_manager_2021_changed, success = "Yes")
##
## 1-sample proportions test with continuity correction
##
## data: ask_a_manager_2021_changed$if_changed [with success = Yes]
<<<<<<< HEAD
## X-squared = 137, df = 1, p-value <2e-16
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
## 0.456 0.469
## sample estimates:
## p
## 0.462
=======
## X-squared = 198, df = 1, p-value <2e-16
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
## 0.449 0.462
## sample estimates:
## p
## 0.455
>>>>>>> XuejingHuang-master
ask_a_manager_age %>%
filter(age_group %in% c("18-24", "25-34" , "35-44" , "45-54" , "55-64" )) %>%
group_by(age_group, gender) %>%
summarise (median_salary_by_age = median (salary),
mean_salary_by_age = mean(salary)) %>%
ggplot () +
geom_col(aes (age_group, median_salary_by_age, fill = gender), position = position_dodge(width = 0.9), alpha = 0.7)+
geom_point(aes(x = age_group, y=mean_salary_by_age, color = gender))+
theme_bw()+
#formatC(x, format = "d")+
labs( title = "Median Salary by Age Group" , y="Salary" ,x = "Age Group") +
NULL
<<<<<<< HEAD
# load population data of US
population_url <- "https://www.ers.usda.gov/webdocs/DataFiles/48747/PopulationEstimates.csv?v=2232"
population <- vroom(population_url) %>%
janitor::clean_names() %>%
# select the latest data, namely 2019
select(fips = fip_stxt, state= area_name, pop_estimate_2019) %>%
group_by(state) %>%
summarise(pop_estimate_2019_m = sum(pop_estimate_2019)/1000000)
reg_data <- ask_a_manager_2021 %>%
filter(!is.na(state)&industry %in% industry_list$industry[1:15]) %>%
group_by(state) %>%
filter(n()>10) %>%
ungroup() %>%
filter(country =="US"¤cy=="USD") %>%
mutate(job = tolower(job_title),
research=grepl("research", job),
manager=grepl("manager", job),
engineer=grepl("engineer", job),
analyst=grepl("analyst", job),
data_related = grepl("data", job),
scientist = grepl("scientist", job),
database = grepl("database", job),
librarian = grepl("librarian", job),
add_context_job = !is.na(additional_context_on_job_title),
add_context_inc = !is.na(additional_context_on_income),
race = case_when(race=="White"~"White",
race=="Asian or Asian American"~"Asian",
race=="Black or African American"~"Black",
T~"Others"),
) %>%
left_join(population,by = c("state")) %>%
select(c("age_group","gender","education","field_exp","pro_exp","race",
"salary","research","manager","engineer","industry","analyst",
"data_related","scientist","database","librarian","add_context_job",
"add_context_inc","pop_estimate_2019_m"))
set.seed(2021)
data_split <- initial_split(reg_data, prop = .7)
data_train <- training(data_split)
data_test <- testing(data_split)
m1 <- lm(salary ~ age_group, data=data_train)
m2 <- lm(salary ~ pro_exp, data=data_train)
m3 <- lm(salary ~ field_exp, data=data_train)
huxreg(m1, m2, m3,
statistics = c('#observations' = 'nobs',
'R squared' = 'r.squared',
'Adj. R Squared' = 'adj.r.squared',
'Residual SE' = 'sigma',
"AIC" = "AIC"),
bold_signif = 0.05,
stars = NULL
) %>%
set_caption('Comparison of models (age or experience?)')
| (1) | (2) | (3) | |
|---|---|---|---|
| (Intercept) | 62543.271 | 69163.194 | 64629.469 |
| (1830.256) | (2757.586) | (1593.274) | |
| age_group25-34 | 19403.664 | ||
| (1903.262) | |||
| age_group35-44 | 29330.903 | ||
| (1921.417) | |||
| age_group45-54 | 30040.880 | ||
| (2092.886) | |||
| age_group55-64 | 24734.760 | ||
| (2580.640) | |||
| age_group65 or over | 33200.979 | ||
| (5904.978) | |||
| pro_exp2 - 4 years | 1748.729 | ||
| (2966.607) | |||
| pro_exp5-7 years | 8977.174 | ||
| (2882.552) | |||
| pro_exp8 - 10 years | 16197.606 | ||
| (2869.375) | |||
| pro_exp11 - 20 years | 22579.448 | ||
| (2818.453) | |||
| pro_exp21 - 30 years | 26381.364 | ||
| (2920.467) | |||
| pro_exp31 - 40 years | 23567.026 | ||
| (3372.684) | |||
| pro_exp41 years or more | 27874.097 | ||
| (6010.020) | |||
| field_exp2 - 4 years | 7854.610 | ||
| (1755.638) | |||
| field_exp5-7 years | 17648.936 | ||
| (1738.610) | |||
| field_exp8 - 10 years | 26244.079 | ||
| (1780.904) | |||
| field_exp11 - 20 years | 32552.800 | ||
| (1736.730) | |||
| field_exp21 - 30 years | 39541.567 | ||
| (2069.468) | |||
| field_exp31 - 40 years | 36020.849 | ||
| (3298.741) | |||
| field_exp41 years or more | 37574.119 | ||
| (8921.330) | |||
| #observations | 11366 | 11366 | 11366 |
| R squared | 0.032 | 0.044 | 0.085 |
| Adj. R Squared | 0.031 | 0.044 | 0.085 |
| Residual SE | 37240.202 | 36996.902 | 36192.227 |
| AIC | 271520.886 | 271373.884 | 270874.011 |
# add more variables to explore and improve accuracy
m4 <- lm(salary ~ field_exp+gender, data=data_train)
m5 <- lm(salary ~ field_exp+gender+gender*field_exp, data=data_train)
m6 <- lm(salary ~ field_exp+gender+education, data=data_train)
m7 <- lm(salary ~ field_exp+gender+gender*field_exp+education+race, data=data_train)
m8 <- lm(salary ~ field_exp+gender+gender*field_exp+education+race+pop_estimate_2019_m, data=data_train)
m9 <- lm(salary ~ field_exp+gender+gender*field_exp+education+race+pop_estimate_2019_m++research+manager+engineer+industry+data_related+scientist+database+librarian, data=data_train)
m10 <- lm(salary ~ field_exp+gender+gender*field_exp+education+race+pop_estimate_2019_m++research+manager+engineer+industry+data_related+scientist+database+librarian+add_context_job+add_context_inc, data=data_train)
m11 <- lm(salary ~ field_exp+gender+gender*field_exp+education+race+pop_estimate_2019_m++research+manager+engineer+industry+data_related+scientist+database+librarian+add_context_job+add_context_inc+data_related*scientist, data=data_train)
huxreg(m4, m5, m6, m7, m8, m9,m10,m11,
statistics = c('#observations' = 'nobs',
'R squared' = 'r.squared',
'Adj. R Squared' = 'adj.r.squared',
'Residual SE' = 'sigma',
"AIC" = "AIC"),
bold_signif = 0.05
) %>%
set_caption('Comparison of models (adding variables)')
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 81776.234 *** | 66767.288 *** | 61868.327 *** | 65401.135 *** | 56250.261 *** | 45055.674 *** | 45729.919 *** | 46004.497 *** |
| (1738.606) | (4349.167) | (3107.656) | (5110.917) | (5079.613) | (4432.007) | (4430.512) | (4429.567) | |
| field_exp2 - 4 years | 7365.950 *** | 14727.862 ** | 7468.093 *** | 16171.417 *** | 16416.344 *** | 13408.778 *** | 13273.262 *** | 13042.230 *** |
| (1718.596) | (4723.099) | (1679.468) | (4573.759) | (4519.084) | (3863.048) | (3859.322) | (3858.449) | |
| field_exp5-7 years | 17031.018 *** | 29579.448 *** | 17169.720 *** | 30678.029 *** | 30514.167 *** | 25868.954 *** | 25696.546 *** | 25568.189 *** |
| (1702.015) | (4670.309) | (1664.254) | (4521.359) | (4467.297) | (3819.817) | (3816.083) | (3814.758) | |
| field_exp8 - 10 years | 25508.925 *** | 42869.715 *** | 25092.637 *** | 44690.433 *** | 44550.091 *** | 39911.766 *** | 39832.038 *** | 39761.758 *** |
| (1743.499) | (4750.322) | (1706.089) | (4600.562) | (4545.550) | (3887.572) | (3883.716) | (3882.214) | |
| field_exp11 - 20 years | 31267.272 *** | 50166.357 *** | 31735.211 *** | 53236.895 *** | 53467.642 *** | 48612.318 *** | 48629.040 *** | 48474.059 *** |
| (1700.925) | (4612.021) | (1665.665) | (4468.249) | (4414.833) | (3777.293) | (3773.497) | (3772.291) | |
| field_exp21 - 30 years | 37108.294 *** | 61997.956 *** | 38462.779 *** | 65992.422 *** | 66619.310 *** | 60928.085 *** | 60794.601 *** | 60636.541 *** |
| (2028.575) | (5051.790) | (1984.661) | (4894.019) | (4835.636) | (4137.108) | (4132.989) | (4131.622) | |
| field_exp31 - 40 years | 34403.010 *** | 54043.409 *** | 36292.138 *** | 57742.688 *** | 58226.660 *** | 48416.441 *** | 48423.945 *** | 48263.287 *** |
| (3229.693) | (7532.978) | (3156.417) | (7297.092) | (7209.882) | (6190.530) | (6184.007) | (6181.720) | |
| field_exp41 years or more | 33149.581 *** | 44622.712 ** | 35725.205 *** | 50250.211 *** | 47405.928 ** | 44952.740 *** | 44480.551 *** | 44360.666 *** |
| (8734.644) | (15065.956) | (8530.996) | (14583.504) | (14410.108) | (12310.473) | (12297.729) | (12292.830) | |
| genderWoman | -19661.624 *** | -2451.366 | -20999.464 *** | -2873.503 | -3218.214 | 4562.148 | 4365.833 | 4216.714 |
| (881.228) | (4657.198) | (864.683) | (4509.702) | (4455.817) | (3809.001) | (3805.147) | (3803.902) | |
| field_exp2 - 4 years:genderWoman | -8184.435 | -9083.216 | -8611.437 | -6486.088 | -6271.595 | -6035.878 | ||
| (5068.882) | (4907.729) | (4849.118) | (4144.534) | (4140.488) | (4139.482) | |||
| field_exp5-7 years:genderWoman | -14283.390 ** | -14158.716 ** | -13537.090 ** | -8833.913 * | -8565.761 * | -8430.754 * | ||
| (5013.483) | (4853.222) | (4795.326) | (4097.453) | (4093.564) | (4092.134) | |||
| field_exp8 - 10 years:genderWoman | -20027.841 *** | -21015.794 *** | -20033.340 *** | -14416.339 *** | -14202.318 *** | -14112.790 *** | ||
| (5105.162) | (4942.695) | (4883.940) | (4174.024) | (4169.914) | (4168.328) | |||
| field_exp11 - 20 years:genderWoman | -22032.484 *** | -23158.969 *** | -22633.855 *** | -15468.431 *** | -15399.157 *** | -15228.537 *** | ||
| (4960.781) | (4803.804) | (4746.459) | (4057.543) | (4053.373) | (4052.094) | |||
| field_exp21 - 30 years:genderWoman | -30413.847 *** | -31079.001 *** | -30930.745 *** | -23740.630 *** | -23457.579 *** | -23280.688 *** | ||
| (5524.145) | (5347.123) | (5283.183) | (4516.219) | (4511.873) | (4510.397) | |||
| field_exp31 - 40 years:genderWoman | -23074.275 ** | -22696.050 ** | -22510.589 ** | -9148.091 | -9115.536 | -8848.865 | ||
| (8341.930) | (8076.384) | (7979.804) | (6844.468) | (6837.204) | (6834.962) | |||
| field_exp41 years or more:genderWoman | -11745.816 | -13193.711 | -11747.941 | -4331.990 | -4543.419 | -4469.599 | ||
| (18526.974) | (17937.090) | (17722.788) | (15141.976) | (15126.328) | (15120.249) | |||
| educationSome college | 4988.324 | 5044.075 | 4636.588 | 2908.226 | 2838.183 | 2817.131 | ||
| (2960.283) | (2927.504) | (2892.597) | (2472.678) | (2470.093) | (2469.106) | |||
| educationCollege degree | 18988.963 *** | 18564.373 *** | 18103.897 *** | 16120.258 *** | 16007.010 *** | 16004.845 *** | ||
| (2707.906) | (2679.020) | (2647.125) | (2271.729) | (2269.421) | (2268.506) | |||
| educationMaster's degree | 22035.333 *** | 22003.130 *** | 21773.117 *** | 26169.277 *** | 26051.736 *** | 26020.462 *** | ||
| (2727.491) | (2698.067) | (2665.835) | (2297.681) | (2295.354) | (2294.450) | |||
| educationProfessional degree (MD, JD, etc.) | 44380.824 *** | 44081.844 *** | 43833.823 *** | 50220.492 *** | 50135.846 *** | 50043.463 *** | ||
| (3062.756) | (3029.627) | (2993.431) | (2679.716) | (2677.263) | (2676.341) | |||
| educationPhD | 34253.662 *** | 34347.772 *** | 33870.649 *** | 42625.750 *** | 42423.359 *** | 42460.846 *** | ||
| (3038.775) | (3005.376) | (2969.572) | (2596.833) | (2594.869) | (2593.850) | |||
| raceBlack | -18402.690 *** | -15938.390 *** | -11313.766 *** | -11407.047 *** | -11518.091 *** | |||
| (2545.570) | (2519.478) | (2155.689) | (2153.584) | (2152.998) | ||||
| raceOthers | -17412.220 *** | -15957.584 *** | -10927.498 *** | -10967.692 *** | -11045.574 *** | |||
| (1872.903) | (1852.565) | (1585.183) | (1583.814) | (1583.364) | ||||
| raceWhite | -21718.588 *** | -18375.693 *** | -12618.968 *** | -12572.226 *** | -12689.214 *** | |||
| (1558.149) | (1552.551) | (1329.726) | (1328.641) | (1328.614) | ||||
| pop_estimate_2019_m | 475.073 *** | 386.143 *** | 384.118 *** | 382.987 *** | ||||
| (28.532) | (24.495) | (24.474) | (24.467) | |||||
| researchTRUE | -883.355 | -798.122 | -289.189 | |||||
| (1755.383) | (1753.641) | (1760.213) | ||||||
| managerTRUE | 8051.040 *** | 7987.988 *** | 7987.374 *** | |||||
| (724.308) | (723.900) | (723.609) | ||||||
| engineerTRUE | 22370.267 *** | 22149.896 *** | 22181.989 *** | |||||
| (1113.630) | (1113.308) | (1112.905) | ||||||
| industryBusiness or Consulting | 13611.797 *** | 13783.351 *** | 13869.657 *** | |||||
| (1733.742) | (1732.446) | (1731.960) | ||||||
| industryComputing or Tech | 20565.078 *** | 20346.082 *** | 20297.335 *** | |||||
| (1210.152) | (1210.497) | (1210.106) | ||||||
| industryEducation (Higher Education) | -25047.782 *** | -24855.369 *** | -24839.356 *** | |||||
| (1309.853) | (1309.556) | (1309.038) | ||||||
| industryEducation (Primary/Secondary) | -28805.465 *** | -29088.985 *** | -29049.552 *** | |||||
| (1676.035) | (1676.591) | (1675.961) | ||||||
| industryEngineering or Manufacturing | -3364.428 * | -3227.936 * | -3138.458 * | |||||
| (1470.645) | (1469.526) | (1469.203) | ||||||
| industryGovernment and Public Administration | -7400.718 *** | -7188.259 *** | -7040.606 *** | |||||
| (1387.959) | (1387.215) | (1387.431) | ||||||
| industryHealth care | -6140.234 *** | -6024.081 *** | -5887.018 *** | |||||
| (1347.309) | (1346.136) | (1346.283) | ||||||
| industryInsurance | -2263.661 | -2285.620 | -2297.536 | |||||
| (1991.211) | (1989.459) | (1988.661) | ||||||
| industryLaw | -6175.522 *** | -6140.885 *** | -6108.539 *** | |||||
| (1790.010) | (1788.228) | (1787.536) | ||||||
| industryMarketing, Advertising & PR | -143.477 | -288.065 | -353.300 | |||||
| (1586.731) | (1585.343) | (1584.837) | ||||||
| industryMedia & Digital | -4530.043 ** | -4428.712 * | -4432.195 * | |||||
| (1744.371) | (1743.891) | (1743.189) | ||||||
| industryNonprofits | -16533.495 *** | -16367.934 *** | -16350.639 *** | |||||
| (1247.272) | (1246.407) | (1245.916) | ||||||
| industryRecruitment or HR | -3839.452 | -4038.510 * | -4082.243 * | |||||
| (2058.276) | (2056.456) | (2055.673) | ||||||
| industryRetail | -21262.973 *** | -21306.608 *** | -21291.698 *** | |||||
| (2109.154) | (2107.490) | (2106.646) | ||||||
| data_relatedTRUE | 7837.394 *** | 7487.863 *** | 5046.085 * | |||||
| (1921.203) | (1920.457) | (2067.321) | ||||||
| scientistTRUE | 14722.590 *** | 14689.589 *** | 8820.476 ** | |||||
| (2639.320) | (2636.522) | (3216.586) | ||||||
| databaseTRUE | -19977.998 ** | -19842.340 ** | -17405.812 ** | |||||
| (6354.838) | (6348.374) | (6391.829) | ||||||
| librarianTRUE | -9214.392 *** | -9369.906 *** | -9397.497 *** | |||||
| (2193.651) | (2192.143) | (2191.277) | ||||||
| add_context_jobTRUE | -2807.597 *** | -2793.119 *** | ||||||
| (627.314) | (627.077) | |||||||
| add_context_incTRUE | 2827.822 ** | 2838.123 ** | ||||||
| (921.745) | (921.379) | |||||||
| data_relatedTRUE:scientistTRUE | 17434.528 ** | |||||||
| (5478.061) | ||||||||
| #observations | 11366 | 11366 | 11366 | 11366 | 11366 | 11366 | 11366 | 11366 |
| R squared | 0.124 | 0.129 | 0.165 | 0.185 | 0.204 | 0.421 | 0.422 | 0.423 |
| Adj. R Squared | 0.123 | 0.128 | 0.164 | 0.183 | 0.203 | 0.419 | 0.420 | 0.421 |
| Residual SE | 35425.743 | 35332.798 | 34591.428 | 34191.042 | 33782.140 | 28843.094 | 28812.405 | 28800.792 |
| AIC | 270388.418 | 270335.691 | 269851.645 | 269596.977 | 269324.476 | 265752.352 | 265730.145 | 265721.976 |
| *** p < 0.001; ** p < 0.01; * p < 0.05. | ||||||||
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| (Intercept) | 73753.850 | 62856.643 | 92165.971 | 70905.720 | 65882.024 |
| (3031.177) | (28236.014) | (3065.946) | (27781.746) | (27527.453) | |
| educationSome college | 1146.369 | 178.194 | 4490.817 | 4192.416 | 4395.104 |
| (3456.755) | (3426.821) | (3410.103) | (3333.414) | (3333.739) | |
| educationCollege degree | 10936.411 | 14478.221 | 16607.004 | 20368.221 | 20329.478 |
| (3096.939) | (3076.147) | (3060.401) | (3001.029) | (2997.480) | |
| educationMaster's degree | 16560.585 | 17400.650 | 23572.779 | 24328.428 | 24326.172 |
| (3128.923) | (3106.455) | (3095.672) | (3036.162) | (3030.978) | |
| educationProfessional degree (MD, JD, etc.) | 60645.762 | 60842.097 | 68008.053 | 70035.175 | 69643.884 |
| (3625.164) | (3597.836) | (3584.357) | (3518.621) | (3508.744) | |
| educationPhD | 30306.472 | 29312.578 | 36070.156 | 39374.432 | 38793.181 |
| (3597.624) | (3568.901) | (3553.194) | (3498.655) | (3478.807) | |
| age_group18-24 | -17669.678 | -9879.743 | -9267.739 | ||
| (28271.961) | (27567.347) | (27491.310) | |||
| age_group25-34 | 1111.921 | -3876.079 | -2008.487 | ||
| (28188.442) | (27463.620) | (27408.375) | |||
| age_group35-44 | 15983.686 | -5471.914 | -1500.703 | ||
| (28187.758) | (27457.952) | (27410.485) | |||
| age_group45-54 | 20370.007 | -11070.008 | -7739.091 | ||
| (28206.456) | (27461.839) | (27431.085) | |||
| age_group55-64 | 25471.862 | -8051.878 | -6100.110 | ||
| (28269.960) | (27527.157) | (27518.041) | |||
| age_group65 or over | 17678.366 | -14583.331 | -14958.135 | ||
| (29120.832) | (28447.729) | (28432.793) | |||
| genderWoman | -30004.937 | -26329.654 | -26229.759 | ||
| (1117.505) | (1099.665) | (1099.280) | |||
| pro_exp2 - 4 years | -7805.873 | ||||
| (4204.339) | |||||
| pro_exp5-7 years | -5300.576 | ||||
| (4351.911) | |||||
| pro_exp8 - 10 years | -1392.495 | ||||
| (4392.389) | |||||
| pro_exp11 - 20 years | 337.324 | ||||
| (4472.274) | |||||
| pro_exp21 - 30 years | -1275.784 | ||||
| (4821.102) | |||||
| pro_exp31 - 40 years | -2767.468 | ||||
| (5781.403) | |||||
| pro_exp41 years or more | -12687.960 | ||||
| (9105.571) | |||||
| field_exp2 - 4 years | 8411.615 | 6500.929 | |||
| (2505.791) | (2225.984) | ||||
| field_exp5-7 years | 17208.320 | 16989.085 | |||
| (2596.075) | (2275.486) | ||||
| field_exp8 - 10 years | 25127.188 | 26530.695 | |||
| (2694.501) | (2345.796) | ||||
| field_exp11 - 20 years | 36182.740 | 37489.263 | |||
| (2725.230) | (2381.517) | ||||
| field_exp21 - 30 years | 57087.061 | 57240.743 | |||
| (3327.637) | (2920.774) | ||||
| field_exp31 - 40 years | 51244.416 | 50426.815 | |||
| (5212.230) | (4654.659) | ||||
| field_exp41 years or more | 49007.992 | 42568.136 | |||
| (12596.801) | (11634.336) | ||||
| #observations | 24899 | 24899 | 24899 | 24899 | 24899 |
| R squared | 0.028 | 0.047 | 0.056 | 0.101 | 0.100 |
| Adj. R Squared | 0.028 | 0.047 | 0.056 | 0.100 | 0.099 |
| Residual SE | 69651.152 | 68979.521 | 68665.310 | 67032.046 | 67051.976 |
# calculate rmse on test set of 11 models
models <- list(m1,m2,m3,m4,m5,m6,m7,m8,m9,m10,m11)
rmses <- c()
i <- 0
for (model in models){
i = i + 1
print(paste("model",i))
preds <- predict(model, data_test)
rmses[i] <- RMSE(
pred = preds,
obs = data_test$salary
)
print(paste("RMSE on train data:",summary(model)$sigma))
print(paste("RMSE on test data:", rmses[i]))
SS.total <- with(data_test,sum((salary-mean(salary))^2))
#SS.residual <- sum(residuals(model)^2)
SS.regression <- sum((preds-mean(data_test$salary))^2)
#SS.total - (SS.regression+SS.residual)
print(paste("R Squared on train data", summary(model)$r.squared))
print(paste("R Squared on test data", SS.regression/SS.total)) # fraction of variation explained by the model
}
## [1] "model 1"
## [1] "RMSE on train data: 37240.2022323879"
## [1] "RMSE on test data: 36621.1660285581"
## [1] "R Squared on train data 0.031567631824252"
## [1] "R Squared on test data 0.0325708057791624"
## [1] "model 2"
## [1] "RMSE on train data: 36996.9022754174"
## [1] "RMSE on test data: 36370.057981252"
## [1] "R Squared on train data 0.0443486164938196"
## [1] "R Squared on test data 0.0453616685733399"
## [1] "model 3"
## [1] "RMSE on train data: 36192.2273555412"
## [1] "RMSE on test data: 35422.5643215129"
## [1] "R Squared on train data 0.0854669794715471"
## [1] "R Squared on test data 0.0871167812847264"
## [1] "model 4"
## [1] "RMSE on train data: 35425.7430731334"
## [1] "RMSE on test data: 34605.928699271"
## [1] "R Squared on train data 0.123870171233949"
## [1] "R Squared on test data 0.128557774630173"
## [1] "model 5"
## [1] "RMSE on train data: 35332.7982526982"
## [1] "RMSE on test data: 34546.9809164511"
## [1] "R Squared on train data 0.12899864129965"
## [1] "R Squared on test data 0.135434006206996"
## [1] "model 6"
## [1] "RMSE on train data: 34591.4284469105"
## [1] "RMSE on test data: 33760.8724115322"
## [1] "R Squared on train data 0.165019603869794"
## [1] "R Squared on test data 0.172745944188134"
## [1] "model 7"
## [1] "RMSE on train data: 34191.0422206893"
## [1] "RMSE on test data: 33460.4589680199"
## [1] "R Squared on train data 0.184955679984239"
## [1] "R Squared on test data 0.195085008342806"
## [1] "model 8"
## [1] "RMSE on train data: 33782.1401858356"
## [1] "RMSE on test data: 33043.2494947125"
## [1] "R Squared on train data 0.204404031635445"
## [1] "R Squared on test data 0.219644384830675"
## [1] "model 9"
## [1] "RMSE on train data: 28843.0937046904"
## [1] "RMSE on test data: 28455.5022244996"
## [1] "R Squared on train data 0.421108713870185"
## [1] "R Squared on test data 0.440785215341321"
## [1] "model 10"
## [1] "RMSE on train data: 28812.4050521705"
## [1] "RMSE on test data: 28412.091845217"
## [1] "R Squared on train data 0.422441983244408"
## [1] "R Squared on test data 0.441748058491165"
## [1] "model 11"
## [1] "RMSE on train data: 28800.7921559706"
## [1] "RMSE on test data: 28397.9853883121"
## [1] "R Squared on train data 0.422958450110671"
## [1] "R Squared on test data 0.442305304536917"
# for model 10, comparing to 28657.84 RMSE in training data.
# the RMSE 29507 is not very large indicating there is not very heavy over fitting problem